Fuzzy human motion analysis: A review
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paper_title: Actions as space-time shapes

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Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach by Gorelick et al. (2004) for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. We show that these features are useful for action recognition, detection and clustering. The method is fast, does not require video alignment and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, non-rigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action and low quality video
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paper_title: Monocular 3d reconstruction of human motion in long action sequences

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A novel algorithm is presented for the 3D reconstruction of human action in long (>30 second) monocular image sequences. A sequence is represented by a small set of automatically found representative keyframes. The skeletal joint positions are manually located in each keyframe and mapped to all other frames in the sequence. For each keyframe a 3D key pose is created, and interpolation between these 3D body poses, together with the incorporation of limb length and symmetry constraints, provides a smooth initial approximation of the 3D motion. This is then fitted to the image data to generate a realistic 3D reconstruction. The degree of manual input required is controlled by the diversity of the sequence’s content. Sports’ footage is ideally suited to this approach as it frequently contains a limited number of repeated actions. Our method is demonstrated on a long (36 second) sequence of a woman playing tennis filmed with a non-stationary camera. This sequence required manual initialisation on <1.5% of the frames, and demonstrates that the system can deal with very rapid motion, severe self-occlusions, motion blur and clutter occurring over several concurrent frames. The monocular 3D reconstruction is verified by synthesising a view from the perspective of a ‘ground truth’ reference camera, and the result is seen to provide a qualitatively accurate 3D reconstruction of the motion.
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paper_title: The Visual Analysis of Human Movement: A Survey

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The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people” is currently one of the most active application domains in computer vision. This survey identifies a number of promising applications and provides an overview of recent developments in this domain. The scope of this survey is limited to work on whole-body or hand motion; it does not include work on human faces. The emphasis is on discussing the various methodologies; they are grouped in 2-D approaches with or without explicit shape models and 3-D approaches. Where appropriate, systems are reviewed. We conclude with some thoughts about future directions.
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paper_title: A Survey of Content-Based Video Retrieval

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This study surveys current trends/methods in video retrieval. The major themes covered by the study include shot segmentation, key frame extraction, feature extraction, clustering, indexing and video retrieval-by similarity, probabilistic, transformational, refinement and relevance feedback. This work has done in an aim to assist the upcoming researchers in the field of video retrieval, to know about the techniques and methods available for video retrieval.
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paper_title: Recognizing human actions: a local SVM approach

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Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper, we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.
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paper_title: Motion-based recognition a survey

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Abstract Motion-based recognition deals with the recognition of an object or its motion based on motion in a sequence of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consists of a complex and coordinated series of events that cannot be understood by looking at only a few frames. This paper provides a review of recent developments in the computer vision aspect of motionbased recognition. We will identify two main steps in motionbased recognition. The first step is the extraction of motion information and its organization into motion models. The second step consists of the matching of some unknown input with a constructed model. Several methods for the recognition of objects and motions will then be reported. They include methods such as cyclic motion detection and recognition, lipreading, hand gestures interpretation, motion verb recognition and temporal textures classification. Tracking and recognition of human motion, like walking, skipping and running will also be discussed. Finally, we will conclude the paper with some thoughts about future directions for motionbased recognition.
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paper_title: Perception of Human Motion

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AbstractHumans, being highly social creatures, rely heavily on the ability to perceive what others are doing and to infer from gestures and expressions what others may be intending to do. These perceptual skills are easily mastered by most, but not all, people, in large part because human action readily communicates intentions and feelings. In recent years, remarkable advances have been made in our understanding of the visual, motoric, and affective influences on perception of human action, as well as in the elucidation of the neural concomitants of perception of human action. This article reviews those advances and, where possible, draws links among those findings.
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paper_title: Multimodal human-computer interaction: A survey

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In this paper we review the major approaches to multimodal human computer interaction from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition, and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for Multimodal Human Computer Interaction (MMHCI) research.
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paper_title: Movement, Activity, and Action: The Role of Knowledge in the Perception of Motion

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This paper presents several approaches to the machine perception of motion and discusses the role and levels of knowledge in each. In particular, different techniques of motion understanding as focusing on one of movement, activity or action are described. Movements are the most atomic primitives, requiring no contextual or sequence knowledge to be recognized; movement is often addressed using either view-invariant or view-specific geometric techniques. Activity refers to sequences of movements or states, where the only real knowledge required is the statistics of the sequence; much of the recent work in gesture understanding falls within this category of motion perception. Finally, actions are larger-scale events, which typically include interaction with the environment and causal relationships; action understanding straddles the grey division between perception and cognition, computer vision and artificial intelligence. These levels are illustrated with examples drawn mostly from the group's work in understanding motion in video imagery. It is argued that the utility of such a division is that it makes explicit the representational competencies and manipulations necessary for perception.
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paper_title: Event-based analysis of video

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Dynamic events can be regarded as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences (possibly of different lengths) based on their behavioral content. This measure is non-parametric and can thus handle a wide range of dynamic events. We use this measure for isolating and clustering events within long continuous video sequences. This is done without prior knowledge of the types of events, their models, or their temporal extent. An outcome of such a clustering process is a temporal segmentation of long video sequences into event-consistent sub-sequences, and their grouping into event-consistent clusters. Our event representation and associated distance measure can also be used for event-based indexing into long video sequences, even when only one short example-clip is available. However, when multiple example-clips of the same event are available (either as a result of the clustering process, or given manually), these can be used to refine the event representation, the associated distance measure, and accordingly the quality of the detection and clustering process.
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paper_title: Recognizing action at a distance

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Our goal is to recognize human action at a distance, at resolutions where a whole person may be, say, 30 pixels tall. We introduce a novel motion descriptor based on optical flow measurements in a spatiotemporal volume for each stabilized human figure, and an associated similarity measure to be used in a nearest-neighbor framework. Making use of noisy optical flow measurements is the key challenge, which is addressed by treating optical flow not as precise pixel displacements, but rather as a spatial pattern of noisy measurements which are carefully smoothed and aggregated to form our spatiotemporal motion descriptor. To classify the action being performed by a human figure in a query sequence, we retrieve nearest neighbor(s) from a database of stored, annotated video sequences. We can also use these retrieved exemplars to transfer 2D/3D skeletons onto the figures in the query sequence, as well as two forms of data-based action synthesis "do as I do" and "do as I say". Results are demonstrated on ballet, tennis as well as football datasets.
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paper_title: The evolution of video surveillance: an overview

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Over the past 10 years, computer vision research has matured significantly. Although some of the core problems, such as object recognition and shape estimation are far from solved, many applications have made considerable progress. Video Surveillance is a thriving example of such an application. On the one hand, worldwide the number of cameras is expected to continue to grow exponentially and security budgets for governments, corporations and the private sector are increasing accordingly. On the other hand, technological advances in target detection, tracking, classification, and behavior analysis improve accuracy and reliability. Simple video surveillance systems that connect cameras via wireless video servers to Home PCs offer simple motion detection capabilities and are on sale at hardware and consumer electronics stores for under $300. The impact of these advances in video surveillance is pervasive. Progress is reported in technical and security publications, abilities are hyped and exaggerated by industry and media, benefits are glamorized and dangers dramatized in movies and politics. This exposure, in turn, enables the expansion of the vocabulary of video surveillance systems paving the way for more general automated video analysis.
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paper_title: Articulated and elastic non-rigid motion: a review

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Motion of physical objects is non-rigid, in general. Most researchers have focused on the study of the motion and structure of rigid objects because of its simplicity and elegance. Recently, investigation of non-rigid structure and motion transformation has drawn the attention of researchers from a wide spectrum of disciplines. Since the non-rigid motion class encompasses a huge domain, we restrict our overview to the motion analysis of articulated and elastic non-rigid objects. Numerous approaches that have been proposed to recover the 3D structure and motion of objects are studied. The discussion includes both: 1) motion recovery without shape models, and 2) model-based analysis, and covers a number of examples of real world objects. >
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paper_title: Intelligent visual surveillance — A survey

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Detection, tracking, and understanding of moving objects of interest in dynamic scenes have been active research areas in computer vision over the past decades. Intelligent visual surveillance (IVS) refers to an automated visual monitoring process that involves analysis and interpretation of object behaviors, as well as object detection and tracking, to understand the visual events of the scene. Main tasks of IVS include scene interpretation and wide area surveillance control. Scene interpretation aims at detecting and tracking moving objects in an image sequence and understanding their behaviors. In wide area surveillance control task, multiple cameras or agents are controlled in a cooperative manner to monitor tagged objects in motion. This paper reviews recent advances and future research directions of these tasks. This article consists of two parts: The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding. The second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems.
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paper_title: Modeling Human Activity From Voxel Person Using Fuzzy Logic

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As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.
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paper_title: Temporal and spatial factors in gait perception that influence gender recognition

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Several temporal and spatial factors affect gender recognition of a walker when portrayed, without familiarity cues, as a dynamic point-light display. We demonstrate that, among temporal parameters, the duration of the dynamic stimulus must be longer than 1.6 sec, but that 2.7 sec is fully adequate. Given the speed of our walkers, the recognition threshold appears to be roughly two step cycles. In addition, presentation rate of the stimulus must be near to normal, perhaps because nonnormal rates alter apparent gravity and obscure the normal relationship between output and conservation of energy. We demonstrate that, among spatial factors, the discreteness of the joint information must be maintained for accurate recognition. We go on to argue that it is the information about the shoulder and the hip of a walker that is of primary importance. Finally, inversion of the stimulus display produces the unexpected effect of reversing the apparent sex of most walkers. That is, when presented upside down, male walkers appear female and female walkers appear male.
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paper_title: Human motion analysis: A review

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Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man?machine interfaces, content-based image storage and retrieval, and video conferencing. This paper gives an overview of the various tasks involved in motion analysis of the human body. We focus on three major areas related to interpreting human motion: (1) motion analysis involving human body parts, (2) tracking a moving human from a single view or multiple camera perspectives, and (3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion from a single view or multiple perspectives focuses on higher-level processing, in which moving humans are tracked without identifying their body parts. After successfully matching the moving human image from one frame to another in an image sequence, understanding the human movements or activities comes naturally, which leads to our discussion of recognizing human activities.
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paper_title: Application of Multimedia to the Study of Human Movement

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Computerised motion analysis is a powerful technique for the diagnosis of abnormalities (especially problems with walking) and monitoring the results of therapy. However, despite the now widespread availability of motion analysis equipment, it has remained primarily a research tool in centres of excellence. This is due to a lack of familiarity of biomechanical terminology amongst clinicians and therapists, and the complexity of the technology required to capture and analyse motion. Using digital video and other multimedia techniques, a software package called Motion Toolbox has been developed, with an intuitive graphical interface. This simplifies and speeds up the analysis and facilitates an intuitive understanding of the results.
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paper_title: Video-Based Abnormal Human Behavior Recognition—A Review

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Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.
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paper_title: Human activity analysis: A review

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Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas.
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paper_title: Advances in View-Invariant Human Motion Analysis: A Review

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As viewpoint issue is becoming a bottleneck for human motion analysis and its application, in recent years, researchers have been devoted to view-invariant human motion analysis and have achieved inspiring progress. The challenge here is to find a methodology that can recognize human motion patterns to reach increasingly sophisticated levels of human behavior description. This paper provides a comprehensive survey of this significant research with the emphasis on view-invariant representation, and recognition of poses and actions. In order to help readers understand the integrated process of visual analysis of human motion, this paper presents recent development in three major issues involved in a general human motion analysis system, namely, human detection, view-invariant pose representation and estimation, and behavior understanding. Public available standard datasets are recommended. The concluding discussion assesses the progress so far, and outlines some research challenges and future directions, and solution to what is essential to achieve the goals of human motion analysis.
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paper_title: Human action recognition using multiple views: a comparative perspective on recent developments

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This paper presents a review and comparative study of recent multi-view 2D and 3D approaches for human action recognition. The approaches are reviewed and categorized due to their nature. We report a comparison of the most promising methods using two publicly available datasets: the INRIA Xmas Motion Acquisition Sequences (IXMAS) and the i3DPost Multi-View Human Action and Interaction Dataset. Additionally, we discuss some of the shortcomings of multi-view camera setups and outline our thoughts on future directions of 3D human action recognition.
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paper_title: The Visual Analysis of Human Movement: A Survey

paper_content:

The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people” is currently one of the most active application domains in computer vision. This survey identifies a number of promising applications and provides an overview of recent developments in this domain. The scope of this survey is limited to work on whole-body or hand motion; it does not include work on human faces. The emphasis is on discussing the various methodologies; they are grouped in 2-D approaches with or without explicit shape models and 3-D approaches. Where appropriate, systems are reviewed. We conclude with some thoughts about future directions.
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paper_title: A Survey of Computer Vision-Based Human Motion Capture

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A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and/or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods presented in the survey. Finally, suggestions for future research directions are offered.
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paper_title: A survey of video datasets for human action and activity recognition

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Highlights? Description of datasets for video-based human activity and action recognition. ? 68 datasets reported: 28 for heterogeneous and 40 for specific human actions. ? Useful data, such as web for dowloading, published works or ground truth, are provided. ? Datasets are compared and classified. Vision-based human action and activity recognition has an increasing importance among the computer vision community with applications to visual surveillance, video retrieval and human-computer interaction. In recent years, more and more datasets dedicated to human action and activity recognition have been created. The use of these datasets allows us to compare different recognition systems with the same input data. The survey introduced in this paper tries to cover the lack of a complete description of the most important public datasets for video-based human activity and action recognition and to guide researchers in the election of the most suitable dataset for benchmarking their algorithms.
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paper_title: A survey of vision-based methods for action representation, segmentation and recognition

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Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human-computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions.
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paper_title: Human behavior analysis in video surveillance: A Social Signal Processing perspective

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The analysis of human activities is one of the most intriguing and important open issues for the automated video surveillance community. Since few years ago, it has been handled following a mere Computer Vision and Pattern Recognition perspective, where an activity corresponded to a temporal sequence of explicit actions (run, stop, sit, walk, etc.). Even under this simplistic assumption, the issue is hard, due to the strong diversity of the people appearance, the number of individuals considered (we may monitor single individuals, groups, crowd), the variability of the environmental conditions (indoor/outdoor, different weather conditions), and the kinds of sensors employed. More recently, the automated surveillance of human activities has been faced considering a new perspective, that brings in notions and principles from the social, affective, and psychological literature, and that is called Social Signal Processing (SSP). SSP employs primarily nonverbal cues, most of them are outside of conscious awareness, like face expressions and gazing, body posture and gestures, vocal characteristics, relative distances in the space and the like. This paper is the first review analyzing this new trend, proposing a structured snapshot of the state of the art and envisaging novel challenges in the surveillance domain where the cross-pollination of Computer Science technologies and Sociology theories may offer valid investigation strategies.
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paper_title: A survey on vision-based human action recognition

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Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.
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paper_title: Human motion analysis: A review

paper_content:

Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man?machine interfaces, content-based image storage and retrieval, and video conferencing. This paper gives an overview of the various tasks involved in motion analysis of the human body. We focus on three major areas related to interpreting human motion: (1) motion analysis involving human body parts, (2) tracking a moving human from a single view or multiple camera perspectives, and (3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion from a single view or multiple perspectives focuses on higher-level processing, in which moving humans are tracked without identifying their body parts. After successfully matching the moving human image from one frame to another in an image sequence, understanding the human movements or activities comes naturally, which leads to our discussion of recognizing human activities.
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paper_title: Representation of Uncertainty in Computer Vision Using Fuzzy Sets

paper_content:

Uncertainty in computer vision can arise at various levels. It can occur in the low level in the raw sensor input, and extends all the way through intermediate and higher levels. Ideally, at any level where decisions are being made on the basis of previous processing steps, a computer vision system must have sufficient flexibility for representation of uncertainty in any of these levels.
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paper_title: Fuzzy Sets in Computer Vision: an Overview

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Every computer vision level crawl with uncertainty, what makes its management a significant problem to be considered and solved when trying for automated systems for scene analysis and interpretation. This is why fuzzy set theory and fuzzy logic is making many inroads into the handling of uncertainty in various aspects of image processing and computer vision. The growth within the use of fuzzy set theory in computer vision is keeping pace with the use of more complex algorithms addressed to solve problems arisen from image vagueness management. Due to the natural linguistic capabilities of high - level computer vision, it is a very appropriate place for applying fuzzy sets. Moreover, scene description, i.e., the language -based representation of regions and their relationships, for either humans or higher automated reasoning provides an excellent opportunity. With this overview we want to address the various aspects of image processing and analysis problems where the theory of fuzzy sets has so far been applied. On the other hand, we will discuss the possibility of making fusion of the merits of fuzzy set theory, neural networks theory and genetic algorithms for improved performance. Finally a list of representative references is also provided.
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paper_title: Fuzzy set theoretic approach to computer vision: An overview

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The authors give an overview of the fuzzy set theoretic approach to computer vision. They discuss the applications of fuzzy set theory in computer vision in the areas of image modeling, preprocessing, segmentation, boundary detection, object/region recognition, and rule-based scene interpretation. >
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paper_title: View and Style-Independent Action Manifolds for Human Activity Recognition

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We introduce a novel approach to automatically learn intuitive and compact descriptors of human body motions for activity recognition. Each action descriptor is produced, first, by applying Temporal Laplacian Eigenmaps to view-dependent videos in order to produce a stylistic invariant embedded manifold for each view separately. Then, all view-dependent manifolds are automatically combined to discover a unified representation which model in a single three dimensional space an action independently from style and viewpoint. In addition, a bidirectional nonlinear mapping function is incorporated to allow projecting actions between original and embedded spaces. The proposed framework is evaluated on a real and challenging dataset (IXMAS), which is composed of a variety of actions seen from arbitrary viewpoints. Experimental results demonstrate robustness against style and view variation and match the most accurate action recognition method.
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paper_title: The Recognition of Human Movement Using Temporal Templates

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A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.
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paper_title: A neural fuzzy system for image motion estimation

paper_content:

Many methods for computing optical flow (image motion vector) have been proposed while others continue to appear. Block-matching methods are widely used because of their simplicity and easy implementation. The motion vector is uniquely defined, in block-matching methods, by the best fit of a small reference subblock from a previous image frame in a larger, search region from the present image frame. Hence, this method is very sensitive to the real environments (involving occlusion, specularity, shadowing, transparency, etc.). In this paper, a neural fuzzy system with robust characteristics and learning ability is incorporated with the block-matching method to make a system adaptive for different circumstances. In the neural fuzzy motion estimation system, each subblock in the search region is assigned a similarity membership contributing different degrees to the motion vector. This system is more reliable, robust, and accurate in motion estimation than many other methods including Horn and Schunck's optical flow, fuzzy logic motion estimator (FME), best block matching, NR, and fast block matching. Since fast block-matching algorithms can be used to reduce search time, a three-step fast search method is employed to find the motion vector in our system. However, the candidate motion vector is often trapped by the local minimum, which makes the motion vector undesirable. An improved three-step fast search method is tested to reduce the effect from local minimum and some comparisons about fast search algorithms are made. In addition, a Quarter Compensation Algorithm for compensating the interframe image to tackle the problem that the motion vector is not an integer but rather a floating point is proposed. Since our system can give the accurate motion vector, we may use the motion information in many different applications such as motion compensation, CCD camera auto-focusing or zooming, moving object extraction, etc. Two application examples will be illustrated in this paper.
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paper_title: Fusing color and texture features for background model

paper_content:

Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel approach that uses fuzzy integral to fuse the texture and color features for background subtraction. The method could handle various small motions of background objects such as swaying tree branches and bushes. Our method requires less computational cost. The model adapts quickly to changes in the scene that enables very sensitive detection of moving targets. The results show that the proposed method is effective and efficient in real-time and accurate background maintenance in complex environment.
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paper_title: Adaptive fuzzy apporach to background modeling using PSO and KLMS

paper_content:

This paper presents a new adaptive fuzzy approach for background estimation in video sequences of complex scene from the function estimation point of view. A Takagi-Sugeno-Kang (TSK) type fuzzy system is used as the function estimator in the study. The proposed approach uses a hybrid learning method combining both the particle swarm optimization (PSO) and the Kernel Least Mean Square (KLMS) to train the fuzzy estimator. In order to estimate background, we first interpret foreground samples as outliers relative to the background ones and so propose an Outlier Separator (OS). Then, the obtained results of OS algorithm are employed in the fuzzy estimator in order to train and estimate background in each pixel. Experimental results show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection for various scenes.
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paper_title: Decision modelling using the Choquet integral

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The usefulness of the Choquet integral for modelling decision under risk and uncertainty is shown. It is shown that some paradoxes of expected utility theory are solved using Choquet integral. It is shown that Choquet expected utility model for decision under uncertainty and rank dependent utility model for decision under risk are respectively same as their simplified version.
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paper_title: Region Based Fuzzy Background Subtraction Using Choquet Integral

paper_content:

Background subtraction, is a widely used method for identifying moving objects in multimedia applications such as video surveillance. Deterministic approaches are the first applications in literature, and they followed statistical approaches; however, more recently prediction-based filter approaches are preferred by researchers. The methods suggested for background subtraction in traffic surveillance applications, which are subject to many uncertainties, such as illumination noise, sudden changes in ambient light and structural changes, have to date failed to satisfy the requirements. Fuzzy approaches in the Artificial Intelligence method are widely used by researchers to eliminate uncertainties within the problem. In this study, a fuzzy background subtraction method, using choquet integral that process certain group of pixels together in order to eliminate uncertainties is suggested. The method is tested on traffic surveillance dataset, leading to satisfying results.
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paper_title: An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure

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Abstract In this paper the non-additivity of Sugeno's fuzzy measures is interpreted in terms of addition and the rationality of the Choquet integral is discussed. It is pointed out that a fuzzy measure on a set X expresses the interaction between the subsets of X and can be represented by an additive measure. It is shown through concrete examples that the Choquet integral is reasonable as an integral with respect to a fuzzy measure. It is also found that the Choquet integral is closely related with the representation of a fuzzy measure.
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paper_title: Type-2 fuzzy mixture of Gaussians model: Application to background modeling

paper_content:

Background modeling is a key step of background subtraction methods used in the context of static camera. The goal is to obtain a clean background and then detect moving objects by comparing it with the current frame. Mixture of Gaussians Model [1] is the most popular technique and presents some limitations when dynamic changes occur in the scene like camera jitter, illumination changes and movement in the background. Furthermore, the MGM is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. To take into account this uncertainty, we propose to use a Type-2 Fuzzy Mixture of Gaussians Model. Results show the relevance of the proposed approach in presence of camera jitter, waving trees and water rippling.
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paper_title: A fuzzy approach for background subtraction

paper_content:

Background Subtraction is a widely used approach to detect moving objects from static cameras. Many different methods have been proposed over the recent years and can be classified following different mathematical model: determinist model, statistical model or filter model. The presence of critical situations i.e. noise, illumination changes and structural background changes introduce two main problems: The first one is the uncertainty in the classification of the pixel in foreground and background. The second one is the imprecision in the localization of the moving object. In this context, we propose a fuzzy approach for background subtraction. For this, we use the Choquet integral in the foreground detection and propose fuzzy adaptive background maintenance. Results show the pertinence of our approach.
---
paper_title: On Sugeno integral as an aggregation function

paper_content:

This paper presents a synthesis on the essential mathematical properties of Choquet and Sugeno integrals viewed as aggregation functions. Some axiomatic characterizations are presented. Subfamilies of the class of those integrals are also investigated.
---
paper_title: Modeling of Dynamic Backgrounds by Type-2 Fuzzy Gaussian Mixture Models

paper_content:

Gaussian Mixture Models (GMMs) are the most popular techniques in background modeling but present some limitations when some dynamic changes occur like camera jitter, illumination changes, movement in the background. Furthermore, the GMM are initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. In this context, we propose to model the background by using a Type-2 Fuzzy Gaussian Mixture Models. The interest is to introduce descriptions of uncertain parameters in the GMM. Experimental validation of the proposed method is performed and presented on a diverse set of RGB and infrared videos. Results show the relevance of the proposed approach.
---
paper_title: A new approach to time series modeling with fuzzy measures and the Choquet integral

paper_content:

In this paper, we propose a new approach to modeling of time series using nonmonotonic fuzzy measures and the Choquet integral, which removes the assumption of linear independency between time series data. As an example, we propose a subset interactive AR model. The modeling consists of a structure identification problem and a parameter identification problem. For structure identification which involves a large number of possible alternative models, we develop a method that uses genetic algorithms to select a subset of regression variables. We apply this model to well-known time series data (i.e., the sunspot numbers), and analyze the quality of the forecast. The results show that the proposed approach is useful for time series analysis with nonlinear features. >
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paper_title: Fuzzy integral for moving object detection

paper_content:

Detection of moving objects is the first step in many applications using video sequences like video-surveillance, optical motion capture and multimedia application. The process mainly used is the background subtraction which one key step is the foreground detection. The goal is to classify pixels of the current image as foreground or background. Some critical situations as shadows, illumination variations can occur in the scene and generate a false classification of image pixels. To deal with the uncertainty in the classification issue, we propose to use the Choquet integral as aggregation operator. Experiments on different data sets in video surveillance have shown a robustness of the proposed method against some critical situations when fusing color and texture features. Different color spaces have been tested to improve the insensitivity of the detection to the illumination changes. Then, the algorithm has been compared with another fuzzy approach based on the Sugeno integral and has proved its robustness.
---
paper_title: A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

paper_content:

The detection of moving objects from stationary cameras is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self-organization through artificial neural networks, that has been shown to well cope with several of the well known issues for background maintenance. Here, we propose a spatial coherence variant to such approach to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection.
---
paper_title: A novel fuzzy background subtraction method based on cellular automata for urban traffic applications

paper_content:

Computational structure of cellular automata has attracted researchers and vastly been used in various fields of science. They are especially suitable for modeling natural systems that can be described as massive collections of simple objects interacting locally with each other, such as motion detection in image processing. On the other hand, extraction of moving objects from an image sequence is a fundamental problem in dynamic image analysis. Nowadays background modeling and subtraction algorithms are commonly used in real-time urban traffic applications for detecting and tracking vehicles and monitoring streets. In this paper by the use of cellular automata, a novel fuzzy approach for background subtraction with a particular interest to the problem of vehicle detection is presented. Our experimental results demonstrate that fuzzy-cellular system is much more efficient, robust and accurate than classical approaches.
---
paper_title: Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos

paper_content:

Mixture of Gaussians (MOG) is the most popular technique for background modeling and presents some limitations when dynamic changes occur in the scene like camera jitter and movement in the background. Furthermore, the MOG is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. In this context, we present a background modeling algorithm based on Type-2 Fuzzy Mixture of Gaussians which is particularly suitable for infrared videos. The use of the Type-2 Fuzzy Set Theory allows to take into account the uncertainty. The results using the OTCBVS benchmark/test dataset videos show the robustness of the proposed method in presence of dynamic backgrounds.
---
paper_title: A Fuzzy System for Background Modeling in Video Sequences

paper_content:

Many applications in video processing require the background modeling as a first step to detect the moving objects in the scene. This paper presents an approach that calculates the updating weight of a recursive adaptive filter using a fuzzy logic system. Simulation results prove the advantages of the fuzzy approach versus conventional methods such as temporal filters.
---
paper_title: A Fuzzy Background Modeling Approach for Motion Detection in Dynamic Backgrounds

paper_content:

Based on Type-2 Fuzzy Gaussian Mixture Model (T2-FGMM) and Markov Random Field (MRF), we propose a novel background modeling method for motion detection in dynamic scenes. The key idea of the proposed approach is the successful introduction of the spatial-temporal constraints into the T2-FGMM by a Bayesian framework. The evaluation results in pixel level demonstrate that the proposed method performs better than the sound Gaussian Mixture Model (GMM) and T2-FGMM in such typical dynamic backgrounds as waving trees and water rippling.
---
paper_title: Type-2 fuzzy mixture of Gaussians model: Application to background modeling

paper_content:

Background modeling is a key step of background subtraction methods used in the context of static camera. The goal is to obtain a clean background and then detect moving objects by comparing it with the current frame. Mixture of Gaussians Model [1] is the most popular technique and presents some limitations when dynamic changes occur in the scene like camera jitter, illumination changes and movement in the background. Furthermore, the MGM is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. To take into account this uncertainty, we propose to use a Type-2 Fuzzy Mixture of Gaussians Model. Results show the relevance of the proposed approach in presence of camera jitter, waving trees and water rippling.
---
paper_title: Type-2 fuzzy Gaussian mixture models

paper_content:

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.
---
paper_title: Robust techniques for background subtraction in urban traffic video

paper_content:

Identifying moving objects from a video sequence is a fundamental and ::: critical task in many computer-vision applications. A common approach ::: is to perform background subtraction, which identifies moving objects ::: from the portion of a video frame that differs significantly from a ::: background model. There are many challenges in developing a good ::: background subtraction algorithm. First, it must be robust against ::: changes in illumination. Second, it should avoid detecting ::: non-stationary background objects such as swinging leaves, rain, snow, ::: and shadow cast by moving objects. Finally, its internal background ::: model should react quickly to changes in background such as starting ::: and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.
---
paper_title: Background subtraction techniques: a review

paper_content:

Background subtraction is a widely used approach for detecting moving objects from static cameras. Many different methods have been proposed over the recent years and both the novice and the expert can be confused about their benefits and limitations. In order to overcome this problem, this paper provides a review of the main methods and an original categorisation based on speed, memory requirements and accuracy. Such a review can effectively guide the designer to select the most suitable method for a given application in a principled way. Methods reviewed include parametric and non-parametric background density estimates and spatial correlation approaches.
---
paper_title: Modeling of Dynamic Backgrounds by Type-2 Fuzzy Gaussian Mixture Models

paper_content:

Gaussian Mixture Models (GMMs) are the most popular techniques in background modeling but present some limitations when some dynamic changes occur like camera jitter, illumination changes, movement in the background. Furthermore, the GMM are initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. In this context, we propose to model the background by using a Type-2 Fuzzy Gaussian Mixture Models. The interest is to introduce descriptions of uncertain parameters in the GMM. Experimental validation of the proposed method is performed and presented on a diverse set of RGB and infrared videos. Results show the relevance of the proposed approach.
---
paper_title: Type-2 Fuzzy Sets Made Simple

paper_content:

Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems. However, they are difficult to understand for a variety of reasons which we enunciate. In this paper, we strive to overcome the difficulties by: (1) establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, (2) presenting a new representation for type-2 fuzzy sets, and (3) using this new representation to derive formulas for union, intersection and complement of type-2 fuzzy sets without having to use the Extension Principle.
---
paper_title: Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos

paper_content:

Mixture of Gaussians (MOG) is the most popular technique for background modeling and presents some limitations when dynamic changes occur in the scene like camera jitter and movement in the background. Furthermore, the MOG is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. In this context, we present a background modeling algorithm based on Type-2 Fuzzy Mixture of Gaussians which is particularly suitable for infrared videos. The use of the Type-2 Fuzzy Set Theory allows to take into account the uncertainty. The results using the OTCBVS benchmark/test dataset videos show the robustness of the proposed method in presence of dynamic backgrounds.
---
paper_title: A Fuzzy Background Modeling Approach for Motion Detection in Dynamic Backgrounds

paper_content:

Based on Type-2 Fuzzy Gaussian Mixture Model (T2-FGMM) and Markov Random Field (MRF), we propose a novel background modeling method for motion detection in dynamic scenes. The key idea of the proposed approach is the successful introduction of the spatial-temporal constraints into the T2-FGMM by a Bayesian framework. The evaluation results in pixel level demonstrate that the proposed method performs better than the sound Gaussian Mixture Model (GMM) and T2-FGMM in such typical dynamic backgrounds as waving trees and water rippling.
---
paper_title: A neural fuzzy system for image motion estimation

paper_content:

Many methods for computing optical flow (image motion vector) have been proposed while others continue to appear. Block-matching methods are widely used because of their simplicity and easy implementation. The motion vector is uniquely defined, in block-matching methods, by the best fit of a small reference subblock from a previous image frame in a larger, search region from the present image frame. Hence, this method is very sensitive to the real environments (involving occlusion, specularity, shadowing, transparency, etc.). In this paper, a neural fuzzy system with robust characteristics and learning ability is incorporated with the block-matching method to make a system adaptive for different circumstances. In the neural fuzzy motion estimation system, each subblock in the search region is assigned a similarity membership contributing different degrees to the motion vector. This system is more reliable, robust, and accurate in motion estimation than many other methods including Horn and Schunck's optical flow, fuzzy logic motion estimator (FME), best block matching, NR, and fast block matching. Since fast block-matching algorithms can be used to reduce search time, a three-step fast search method is employed to find the motion vector in our system. However, the candidate motion vector is often trapped by the local minimum, which makes the motion vector undesirable. An improved three-step fast search method is tested to reduce the effect from local minimum and some comparisons about fast search algorithms are made. In addition, a Quarter Compensation Algorithm for compensating the interframe image to tackle the problem that the motion vector is not an integer but rather a floating point is proposed. Since our system can give the accurate motion vector, we may use the motion information in many different applications such as motion compensation, CCD camera auto-focusing or zooming, moving object extraction, etc. Two application examples will be illustrated in this paper.
---
paper_title: Fusing color and texture features for background model

paper_content:

Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel approach that uses fuzzy integral to fuse the texture and color features for background subtraction. The method could handle various small motions of background objects such as swaying tree branches and bushes. Our method requires less computational cost. The model adapts quickly to changes in the scene that enables very sensitive detection of moving targets. The results show that the proposed method is effective and efficient in real-time and accurate background maintenance in complex environment.
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paper_title: Adaptive fuzzy apporach to background modeling using PSO and KLMS

paper_content:

This paper presents a new adaptive fuzzy approach for background estimation in video sequences of complex scene from the function estimation point of view. A Takagi-Sugeno-Kang (TSK) type fuzzy system is used as the function estimator in the study. The proposed approach uses a hybrid learning method combining both the particle swarm optimization (PSO) and the Kernel Least Mean Square (KLMS) to train the fuzzy estimator. In order to estimate background, we first interpret foreground samples as outliers relative to the background ones and so propose an Outlier Separator (OS). Then, the obtained results of OS algorithm are employed in the fuzzy estimator in order to train and estimate background in each pixel. Experimental results show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection for various scenes.
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paper_title: Detecting Pedestrian Abnormal Behavior Based on Fuzzy Associative Memory

paper_content:

Visual analysis of human motion in video sequences has attracted more and more attention to computer visions in recent years. In order to indicate the pedestrian movement in Intelligent Security Monitoring System, an articulated model of human is presented. According to the contour angle movement of bodypsilas major organs, a fuzzy function is designed. Fuzzy Associative Memory (FAM) is proposed to infer abnormal behavior of the walker. The overall degree of the anomaly is resulted from the fuzzy membership of the pedestrian's organ using a three layer FAM. In the realization of the system a combined method of centroid and fuzzy discriminant is presented. Fuzzy discriminant can detect irregularities and implements initiative analysis of body's behaviors in visual surveillance. Therefore, we can recognize some abnormal behaviors and then alarm. The results show that the new algorithm has better performance.
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paper_title: Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application

paper_content:

A new classification approach for human body postures based on a neural fuzzy network is proposed in this paper, and the approach is applied to detect emergencies that are caused by accidental falls. Four main body postures are used for posture classification, including standing, bending, sitting, and lying. After the human body is segmented from the background, the classification features are extracted from the silhouette. The body silhouette is projected onto horizontal and vertical axes, and then, a discrete Fourier transform is applied to each projected histogram. Magnitudes of significant Fourier transform coefficients together with the silhouette length-width ratio are used as features. The classifier is designed by a neural fuzzy network. The four postures can be classified with high accuracy according to experimental results. Classification results are also applicable to home care emergency detection of a person who suddenly falls and remains in the lying posture for a period of time due to experiments that were performed.
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paper_title: Human–Computer Interaction for Smart Environment Applications Using Fuzzy Hand Posture and Gesture Models

paper_content:

Ever since the assemblage of the first computer, efforts have been made to improve the way people could use machines. Recently, the usage of smart environments has become popular in order to make everyday living more comfortable and to improve the quality of living of humans. In this paper, a hand posture and gesture modeling and recognition system is introduced, which can be used as an interface to make possible communication with smart environment (intelligent space) by simple hand gestures. The system transforms preprocessed data of the detected hand into a fuzzy hand-posture feature model by using fuzzy neural networks and based on this model determines the actual hand posture applying fuzzy inference. Finally, from the sequence of detected hand postures, the system can recognize the hand gesture of the user.
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paper_title: Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding

paper_content:

The psychological overcharge issue related to human inadequacy to maintain a constant level of attention in simultaneously monitoring multiple visual information sources makes necessary to develop enhanced video surveillance systems that automatically understand human behaviors and identify dangerous situations. This paper introduces a semantic human behavioral analysis (HBA) system based on a neuro-fuzzy approach that, independently from the specific application, translates tracking kinematic data into a collection of semantic labels characterizing the behavior of different actors in a scene in order to appropriately classify the current situation. Different from other HBA approaches, the proposed system shows high level of scalability, robustness and tolerance for tracking imprecision and, for this reason, it could represent a valid choice for improving the performance of current systems.
---
paper_title: A fuzzy approach for background subtraction

paper_content:

Background Subtraction is a widely used approach to detect moving objects from static cameras. Many different methods have been proposed over the recent years and can be classified following different mathematical model: determinist model, statistical model or filter model. The presence of critical situations i.e. noise, illumination changes and structural background changes introduce two main problems: The first one is the uncertainty in the classification of the pixel in foreground and background. The second one is the imprecision in the localization of the moving object. In this context, we propose a fuzzy approach for background subtraction. For this, we use the Choquet integral in the foreground detection and propose fuzzy adaptive background maintenance. Results show the pertinence of our approach.
---
paper_title: Combining Fuzzy Vector Quantization With Linear Discriminant Analysis for Continuous Human Movement Recognition

paper_content:

In this paper, a novel method for continuous human movement recognition based on fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) is proposed. We regard a movement as a unique combination of basic movement patterns, the so-called dynemes. The proposed algorithm combines FVQ and LDA to discover the most discriminative dynemes as well as represent and discriminate the different human movements in terms of these dynemes. This method allows for simple Mahalanobis or cosine distance comparison of not aligned human movements, taking into account implicitly time shifts and internal speed variations, and, thus, aiding the design of a real-time continuous human movement recognition algorithm. The effectiveness and robustness of this method is shown by experimental results on a standard dataset with videos captured under real conditions, and on a new video dataset created using motion capture data.
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paper_title: Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

paper_content:

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.
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paper_title: Recognition of gestures in Arabic sign language using neuro-fuzzy systems

paper_content:

Hand gestures play an important role in communication between people during their daily lives. But the extensive use of hand gestures as a mean of communication can be found in sign languages. Sign language is the basic communication method between deaf people. A translator is usually needed when an ordinary person wants to communicate with a deaf one. The work presented in this paper aims at developing a system for automatic translation of gestures of the manual alphabets in the Arabic sign language. In doing so, we have designed a collection of ANFIS networks, each of which is trained to recognize one gesture. Our system does not rely on using any gloves or visual markings to accomplish the recognition job. Instead, it deals with images of bare hands, which allows the user to interact with the system in a natural way. An image of the hand gesture is processed and converted into a set of features that comprises of the lengths of some vectors which are selected to span the fingertips' region. The extracted features are rotation, scale, and translation invariat, which makes the system more flexible. The subtractive clustering algorithm and the least-squares estimator are used to identify the fuzzy inference system, and the training is achieved using the hybrid learning algorithm. Experiments revealed that our system was able to recognize the 30 Arabic manual alphabets with an accuracy of 93.55%.
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paper_title: Fuzzy Running Average and Fuzzy Background Subtraction: Concepts and Application

paper_content:

Summary Running average method and its modified version are two simple and fast methods for background modeling. In this paper, some weaknesses of running average method and standard background subtraction are mentioned. Then, a fuzzy approach for background modeling and background subtraction is proposed. For fuzzy background modeling, fuzzy running average is suggested. Background modeling and background subtraction algorithms are very commonly used in vehicle detection systems. To demonstrate the advantages of fuzzy running average and fuzzy background subtraction, these methods and their standard versions are compared in vehicle detection application. Experimental results show that fuzzy approach is relatively more accurate than classical approach.
---
paper_title: Fuzzy integral for moving object detection

paper_content:

Detection of moving objects is the first step in many applications using video sequences like video-surveillance, optical motion capture and multimedia application. The process mainly used is the background subtraction which one key step is the foreground detection. The goal is to classify pixels of the current image as foreground or background. Some critical situations as shadows, illumination variations can occur in the scene and generate a false classification of image pixels. To deal with the uncertainty in the classification issue, we propose to use the Choquet integral as aggregation operator. Experiments on different data sets in video surveillance have shown a robustness of the proposed method against some critical situations when fusing color and texture features. Different color spaces have been tested to improve the insensitivity of the detection to the illumination changes. Then, the algorithm has been compared with another fuzzy approach based on the Sugeno integral and has proved its robustness.
---
paper_title: Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps

paper_content:

A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a piecewise-continuous function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. >
---
paper_title: A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

paper_content:

The detection of moving objects from stationary cameras is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self-organization through artificial neural networks, that has been shown to well cope with several of the well known issues for background maintenance. Here, we propose a spatial coherence variant to such approach to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection.
---
paper_title: Hand Gesture Recognition Using Fuzzy Neural Network

paper_content:

In this paper, we introduce a new approach to gesture recognition based on incorporating the idea of fuzzy ARTMAP [1] in the feature recognition neural network and Hussain and Kabuka [3]. It has already shown that neural networks are relatively effective for partially corrupted images. However, a distinct subnet is created for every training pattern. Therefore, a big network is obtained when the number training patterns is large. Furthermore, recognition rate can be hurt due to the failure of combining features from similar training patterns. We would like to improve this technique by considering input images as fuzzy resources for hand gesture recognition. Training patterns are allowed to be merged, based on the measure of similarity among features, resulting in a subnet being shared by similar patterns and network size is reduced and recognition rate is increased. The high gesture recognition rates and quick network retraining times found in the present study suggest that a neural network approach to gesture recognition be further evaluated.
---
paper_title: A novel fuzzy background subtraction method based on cellular automata for urban traffic applications

paper_content:

Computational structure of cellular automata has attracted researchers and vastly been used in various fields of science. They are especially suitable for modeling natural systems that can be described as massive collections of simple objects interacting locally with each other, such as motion detection in image processing. On the other hand, extraction of moving objects from an image sequence is a fundamental problem in dynamic image analysis. Nowadays background modeling and subtraction algorithms are commonly used in real-time urban traffic applications for detecting and tracking vehicles and monitoring streets. In this paper by the use of cellular automata, a novel fuzzy approach for background subtraction with a particular interest to the problem of vehicle detection is presented. Our experimental results demonstrate that fuzzy-cellular system is much more efficient, robust and accurate than classical approaches.
---
paper_title: A Fuzzy System for Background Modeling in Video Sequences

paper_content:

Many applications in video processing require the background modeling as a first step to detect the moving objects in the scene. This paper presents an approach that calculates the updating weight of a recursive adaptive filter using a fuzzy logic system. Simulation results prove the advantages of the fuzzy approach versus conventional methods such as temporal filters.
---
paper_title: Learning activity patterns using fuzzy self-organizing neural network

paper_content:

Activity understanding in visual surveillance has attracted much attention in recent years. In this paper, we present a new method for learning patterns of object activities in image sequences for anomaly detection and activity prediction. The activity patterns are constructed using unsupervised learning of motion trajectories and object features. Based on the learned activity patterns, anomaly detection and activity prediction can be achieved. Unlike existing neural network based methods, our method uses a whole trajectory as an input to the network. This makes the network structure much simpler. Furthermore, the fuzzy set theory based method and the batch learning method are introduced into the network learning process, and make the learning process much more efficient. Two sets of data acquired, respectively, from a model scene and a campus scene are both used to test the proposed algorithms. Experimental results show that the fuzzy self-organizing neural network (fuzzy SOM) is much more efficient than the Kohonen self-organizing feature map (SOFM) and vector quantization in both speed and accuracy, and the anomaly detection and activity prediction algorithms have encouraging performances.
---
paper_title: A novel feature recognition neural network and its application to character recognition

paper_content:

Presents a feature recognition network for pattern recognition that learns the patterns by remembering their different segments. The base algorithm for this network is a Boolean net algorithm that the authors developed during past research. Simulation results show that the network can recognize patterns after significant noise, deformation, translation and even scaling. The network is compared to existing popular networks used for the same purpose, especially the Neocognitron. The network is also analyzed as regards to interconnection complexity and information storage/retrieval. >
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paper_title: Background subtraction and human detection in outdoor videos using fuzzy logic

paper_content:

This paper presents a scale and view invariant approach for human detection in the presence of various other objects like animals, vehicles, etc. Human detection is one of the essential steps in applications like activity recognition, gait recognition, human centric surveillance etc. Inaccurate detection of humans in such applications may increase the number of false alarms. In the proposed work, fuzzy logic has been used to model a robust background for object detection. Three different features are extracted from the contours of the detected objects. These features are aggregated using fuzzy inference system. Then human contour is identified using template matching. The proposed method consists of four main steps; Moving Object Detection, Feature Extraction, Feature Aggregation, and Human Contour Detection.
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paper_title: An Introduction to Fuzzy Logic Applications in Intelligent Systems

paper_content:

Knowledge Representation in Fuzzy Logic L.A. Zadeh. Expert Systems Using Fuzzy Logic R.R. Yager. Fuzzy Rules in Knowledge-Based Systems D. Dubois, H. Prade. Fuzzy Logic Controllers H. Berenji. Methods and Applications of Fuzzy Mathematical Programming H.J. Zimmermann. Fuzzy Set Methods in Computer Vision J.M. Keller, R. Krishnapuram. Fuzziness, Image Information and Scene Analysis S.K. Pal. Fuzzy Sets in Natural Language Processing V. Novak. Fuzzy-Set-Theoretic Applications in Modeling of Man-Machine Interactions W. Karwowski, G. Salvendy. Questionnaires and Fuzziness B. Bouchon-Meunier. Fuzzy Logic Knowledge Systems and Artificial Neural Networks in Medicine and Biology E. Sanchez. The Representation and Use of Uncertainty and Metaknowledge in Milord R. Lopez de Montaras, C. Sierra, J. Augusti. Fuzzy Logic with Linguistic Quantifiers in Group Decision Making J. Kacprzyk, M. Fedrizzi, H. Nurmi. Learning in Uncertain Environments M. Botta, A. Giordana, L. Saitta. Evidential Reasoning Under Probabilistic and Fuzzy Uncertainties J.F. Baldwin. Probabilistic Sets-Probabilistic Extension of Fuzzy Sets K. Hirota. Index.
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paper_title: Linguistic summarization of video for fall detection using voxel person and fuzzy logic

paper_content:

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person's states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.
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paper_title: Detection of human presence in a surveillance video using fuzzy approach

paper_content:

Surveillance systems have become increasingly popular in the globalization process. Intelligent video surveillance system based on image recognition is widely used to effectively prevent many crimes and helps to provide public security. Due to the high complexity in techniques such as real time processing and image contents analysis/understanding, a well-developed product is not available until now in this field. In this work, human presence is detected from the surveillance video by extracting the skin region (if present) from the selected frame. Since human skin can be found in a varied color, fuzzy approach is used to extract the skin region. The experimental result shows the effectiveness of the proposed technique.
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paper_title: Extension of a soft-computing framework for activity analysis from linguistic summarizations of video

paper_content:

Video cameras are a relatively low-cost, rich source of information that can be used for ldquowell-beingrdquo assessment and abnormal event detection for the goal of allowing elders to live longer and healthier independent lives. We previously reported a soft-computing fall detection system, based on two levels from a hierarchy of fuzzy inference using linguistic summarizations of activity acquired temporally from a three dimensional voxel representation of the human found by back projecting silhouettes acquired from multiple cameras. This framework is extremely flexible and rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. In this paper, we show the flexibility of our activity analysis framework by extending it to additional common elderly activities and contextual awareness is added for reasoning based on location or static objects in the apartment.
---
paper_title: Human motion detection using fuzzy rule-base classification of moving blob regions

paper_content:

The task of detecting and classifying human motion is an important preliminary tool for many high-level applications. However, many approaches suffer from the lack of robust classification and proper motion cues. This paper presents a novel human motion detection algorithm that uses a fuzzy rule-base classification scheme based on moving blob regions. This approach first obtains a motion image through the acquisition and segmentation of video sequences. Then, preprocessing is applied to the motion image before major blobs are identified. Using motion estimation and ellipse fitting, three blob characteristics are extracted from the major blobs as classification criteria. These characteristics are used as inputs to a fuzzy rule-base for classification of the detected motion. Through experimental evaluation – a database test and real-time field test, the implemented system achieved good detection rates in both tests at efficient real-time speeds. In comparison with earlier approaches, this algorithm also managed better detection rates. Above all, the performance of the proposed algorithm has demonstrated its feasibility for an effective real-time implementation.
---
paper_title: View and Style-Independent Action Manifolds for Human Activity Recognition

paper_content:

We introduce a novel approach to automatically learn intuitive and compact descriptors of human body motions for activity recognition. Each action descriptor is produced, first, by applying Temporal Laplacian Eigenmaps to view-dependent videos in order to produce a stylistic invariant embedded manifold for each view separately. Then, all view-dependent manifolds are automatically combined to discover a unified representation which model in a single three dimensional space an action independently from style and viewpoint. In addition, a bidirectional nonlinear mapping function is incorporated to allow projecting actions between original and embedded spaces. The proposed framework is evaluated on a real and challenging dataset (IXMAS), which is composed of a variety of actions seen from arbitrary viewpoints. Experimental results demonstrate robustness against style and view variation and match the most accurate action recognition method.
---
paper_title: The Recognition of Human Movement Using Temporal Templates

paper_content:

A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.
---
paper_title: Adaptive Silouette Extraction and Human Tracking in Complex and Dynamic Environments

paper_content:

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction and human tracking algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In this work, we propose to address the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extract features from image regions, accumulate the feature information over time, fuse the high-level knowledge with low-level features, and build a time-varying background model. We develop a fuzzy decision process to detach foreground moving objects from the human body. Our experimental results demonstrate that the algorithm is very efficient and robust.
---
paper_title: A Fuzzy Spatio-Temporal-based Approach for Activity Recognition

paper_content:

Over the last decade, there has been a significant deployment of systems dedicated to surveillance. These systems make use of real-time sensors that generate continuous streams of data. Despite their success in many cases, the increased number of sensors leads to a cognitive overload for the operator in charge of their analysis. However, the context and the application requires an ability to react in real-time. The research presented in this paper introduces a spatio-temporal-based approach the objective of which is to provide a qualitative interpretation of the behavior of an entity (e.g., a human or vehicle). The process is formally supported by a fuzzy logic-based approach, and designed in order to be as generic as possible.
---
paper_title: A fuzzy logic-based system for the automation of human behavior recognition using machine vision in intelligent environments

paper_content:

The recent years have witnessed significant progress in the automation of human behavior recognition using machine vision in order to realize intelligent environments which are capable of detecting users' actions and gestures so that the needed services can be provided automatically and instantly for maximizing the user comfort and safety as well as minimizing energy. However, the majority of traditional human behavior machine vision-based recognition approaches rely on assumptions (such as known spatial locations and temporal segmentations) or computationally expensive approaches (such as sliding window search through a spatio-temporal volume). Hence, it is difficult for such methods to scale up and handle the high uncertainty levels and complexities available in real-world applications. This paper proposes a novel fuzzy machine vision-based framework for efficient humans' behavior recognition. A model-based feature set is utilized to extract visual feature cues including silhouette slices and movement speed from the human silhouette in video sequences which are analyzed as inputs by the proposed fuzzy system. We have employed fuzzy c-means clustering to learn the membership functions of the proposed fuzzy system. The behavior recognition was implemented via selecting the best candidate's behavior category with the highest output degree as the recognized behavior. We have successfully tested our system on the publicly available Weizmann human action dataset where our fuzzy-based system produced an average recognition accuracy of 94.03 %, which outperformed the traditional non-fuzzy systems based on hidden Markov models and other state-of-the-art approaches which were applied on the Weizmann dataset.
---
paper_title: Adaptive Silhouette Extraction in Dynamic Environments Using Fuzzy Logic

paper_content:

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In a previous paper, we addressed some of the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved by a human are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the human silhouette, such as in a quick arm movement. In this paper, we develop a fuzzy logic inference system to detach the silhouette of a moving object from the human body. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.
---
paper_title: Modeling Human Activity From Voxel Person Using Fuzzy Logic

paper_content:

As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.
---
paper_title: Background subtraction and human detection in outdoor videos using fuzzy logic

paper_content:

This paper presents a scale and view invariant approach for human detection in the presence of various other objects like animals, vehicles, etc. Human detection is one of the essential steps in applications like activity recognition, gait recognition, human centric surveillance etc. Inaccurate detection of humans in such applications may increase the number of false alarms. In the proposed work, fuzzy logic has been used to model a robust background for object detection. Three different features are extracted from the contours of the detected objects. These features are aggregated using fuzzy inference system. Then human contour is identified using template matching. The proposed method consists of four main steps; Moving Object Detection, Feature Extraction, Feature Aggregation, and Human Contour Detection.
---
paper_title: Type-2 fuzzy logic systems: type-reduction

paper_content:

Type-reduction in a type-2 fuzzy logic system (FLS) is an "extended" version of the defuzzification operation in a type-1 FLS. In this paper, we briefly review the structure of a type-2 FLS and describe type-reduction in detail. We focus on a center-of-sets type-reducer, and provide some examples to illustrate it. We also provide some practical approximations to type-reduction computations for certain type-2 membership functions.
---
paper_title: Adaptive Silouette Extraction and Human Tracking in Complex and Dynamic Environments

paper_content:

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction and human tracking algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In this work, we propose to address the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extract features from image regions, accumulate the feature information over time, fuse the high-level knowledge with low-level features, and build a time-varying background model. We develop a fuzzy decision process to detach foreground moving objects from the human body. Our experimental results demonstrate that the algorithm is very efficient and robust.
---
paper_title: Interval type-2 fuzzy logic systems: theory and design

paper_content:

We present the theory and design of interval type-2 fuzzy logic systems (FLSs). We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs: one that is based on a general inference formula for them. We introduce the concept of upper and lower membership functions (MFs) and illustrate our efficient inference method for the case of Gaussian primary MFs. We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting when a nonstationary time-series is corrupted by additive noise where SNR is uncertain and demonstrate an improved performance over type-1 FLSs.
---
paper_title: Adaptive Silhouette Extraction in Dynamic Environments Using Fuzzy Logic

paper_content:

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In a previous paper, we addressed some of the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved by a human are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the human silhouette, such as in a quick arm movement. In this paper, we develop a fuzzy logic inference system to detach the silhouette of a moving object from the human body. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.
---
paper_title: An Interval Type-2 Fuzzy Logic System for Human Silhouette Extraction in Dynamic Environments

paper_content:

In this paper, we present a type-2 fuzzy logic based system for robustly extracting the human silhouette which is a fundamental and important procedure for advanced video processing applications, such as pedestrian tracking, human activity analysis and event detection. The presented interval type-2 fuzzy logic system is able to detach moving objects from extracted human silhouette in dynamic environments. Our real-world experimental results demonstrate that the proposed interval type-2 fuzzy logic system works effectively and efficiently for moving objects detachment where the type-2 approach outperforms the type-1 fuzzy system while significantly reducing the misclassification when compared to the type-1 fuzzy system.
---
paper_title: Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding

paper_content:

The psychological overcharge issue related to human inadequacy to maintain a constant level of attention in simultaneously monitoring multiple visual information sources makes necessary to develop enhanced video surveillance systems that automatically understand human behaviors and identify dangerous situations. This paper introduces a semantic human behavioral analysis (HBA) system based on a neuro-fuzzy approach that, independently from the specific application, translates tracking kinematic data into a collection of semantic labels characterizing the behavior of different actors in a scene in order to appropriately classify the current situation. Different from other HBA approaches, the proposed system shows high level of scalability, robustness and tolerance for tracking imprecision and, for this reason, it could represent a valid choice for improving the performance of current systems.
---
paper_title: Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

paper_content:

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.
---
paper_title: Kinematics-based tracking of human walking in monocular video sequences

paper_content:

Human tracking is currently one of the most active research topics in computer vision. This paper proposed a kinematics-based approach to recovering motion parameters of people walking from monocular video sequences using robust image matching and hierarchical search. Tracking a human with unconstrained movements in monocular image sequences is extremely challenging. To reduce the search space, we design a hierarchical search strategy in a divide-and-conquer fashion according to the tree-like structure of the human body model. Then a kinematics-based algorithm is proposed to recursively refine the joint angles. To measure the matching error, we present a pose evaluation function combining both boundary and region information. We also address the issue of initialization by matching the first frame to six key poses acquired by clustering and the pose having minimal matching error is chosen as the initial pose. Experimental results in both indoor and outdoor scenes demonstrate that our approach performs well. (C) 2004 Published by Elsevier B.V.
---
paper_title: Cardboard people: a parameterized model of articulated image motion

paper_content:

We extend the work of Black and Yacoob (1995) on the tracking and recognition of human facial expressions using parametrized models of optical flow to deal with the articulated motion of human limbs. We define a "card-board person model" in which a person's limbs are represented by a set of connected planar patches. The parametrized image motion of these patches in constrained to enforce articulated motion and is solved for directly using a robust estimation technique. The recovered motion parameters provide a rich and concise description of the activity that can be used for recognition. We propose a method for performing view-based recognition of human activities from the optical flow parameters that extends previous methods to cope with the cyclical nature of human motion. We illustrate the method with examples of tracking human legs of long image sequences.
---
paper_title: Local and Global Skeleton Fitting Techniques for Optical Motion Capture , Modeling and Motion Capture Techniques for Virtual Environments

paper_content:

Reference CVLAB-CONF-1998-008 URL: http://cvlab.epfl.ch/publications/publications/1998/SilaghiRRPD98.pdf Record created on 2006-02-03, modified on 2017-05-12
---
paper_title: The Visual Analysis of Human Movement: A Survey

paper_content:

The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people” is currently one of the most active application domains in computer vision. This survey identifies a number of promising applications and provides an overview of recent developments in this domain. The scope of this survey is limited to work on whole-body or hand motion; it does not include work on human faces. The emphasis is on discussing the various methodologies; they are grouped in 2-D approaches with or without explicit shape models and 3-D approaches. Where appropriate, systems are reviewed. We conclude with some thoughts about future directions.
---
paper_title: Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection

paper_content:

We present a new method for the 3D model-based tracking of human body parts. To mitigate the difficulties arising due to occlusion among body parts, we employ multiple calibrated cameras in a mutually orthogonal configuration. In addition, we develop criteria for a time varying active selection of a set of cameras to track the motion of a particular human part. In particular, at every frame, each camera tracks a number of parts depending on the visibility of these parts and the observability of their predicted motion from the specific camera. To relate points on the occluding contours of the parts to points on their models we apply concepts from projective geometry. Then, within the physics-based framework we compute the generalized forces applied from the parts' occluding contours to model points of the body parts. These forces update the translational and rotational degrees of freedom of the model, such as to minimize the discrepancy between the sensory data and the estimated model state. We present initial tracking results from a series of experiments involving the recovery of complex 3D motions in the presence of significant occlusion.
---
paper_title: Model-based tracking of self-occluding articulated objects

paper_content:

Computer sensing of hand and limb motion is an important problem for applications in human computer interaction and computer graphics. We describe a framework for local trading of self occluding motion, in which one part of an object obstructs the visibility of another. Our approach uses a kinematic model to predict occlusions and windowed templates to track partially occluded objects. We present offline 3D tracking results for hand motion with significant self occlusion. >
---
paper_title: A Survey of Computer Vision-Based Human Motion Capture

paper_content:

A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and/or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods presented in the survey. Finally, suggestions for future research directions are offered.
---
paper_title: Tracking of persons in monocular image sequences

paper_content:

We present an approach to extract quantitative geometric descriptions of the movements of persons by fitting the projection of a 3-dimensional human model to consecutive frames of an image sequence. The kinematics of the human model is given by a homogeneous transformation tree and its body parts are modeled by right-elliptical cones. The proposed approach can determine the values of a varying number of degrees of freedom (DOFs; body joints, position and orientation of the person relative to the camera) according to the application and the kind of image sequence. The determination of the DOFs is understood as an estimation problem which is solved by an iterated extended Kalman filter (IEKF). For this purpose, the human model is augmented by a simple motion model of constant velocity for all DOFs which is used in the prediction step of the IEKF. In the update step both region and edge information is used.
---
paper_title: Human motion analysis: A review

paper_content:

Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man?machine interfaces, content-based image storage and retrieval, and video conferencing. This paper gives an overview of the various tasks involved in motion analysis of the human body. We focus on three major areas related to interpreting human motion: (1) motion analysis involving human body parts, (2) tracking a moving human from a single view or multiple camera perspectives, and (3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion from a single view or multiple perspectives focuses on higher-level processing, in which moving humans are tracked without identifying their body parts. After successfully matching the moving human image from one frame to another in an image sequence, understanding the human movements or activities comes naturally, which leads to our discussion of recognizing human activities.
---
paper_title: Towards model-based recognition of human movements in image sequences

paper_content:

The interpretation of the movements of articulated bodies in image sequences is one of the most challenging problems in computer vision. In this contribution, we introduce a model-based approach for the recognition of pedestrians. We represent the human body by a 3D-model consisting of cylinders, whereas for modelling the movement of walking we use data from medical motion studies. The estimation of model parameters in consecutive images is done by applying a Kalman filter. Experimental results are shown for synthetic as well as for real image data.
---
paper_title: First Sight: A human body outline labeling system

paper_content:

First Sight, a vision system in labeling the outline of a moving human body, is proposed in this paper. The emphasis of First Sight is on the analysis of motion information gathered solely from the outline of a moving human object. Two main processes are implemented in First Sight. The first process uses a novel technique to extract the outline of a moving human body from an image sequence. The second process, which employs a new human body model, interprets the outline and produces a labeled two-dimensional human body stick figure for each frame of the image sequence. Extensive knowledge of the structure, shape, and posture of the human body is used in the model. The experimental results of applying the technique on unedited image sequences with self-occlusions and missing boundary lines are encouraging. >
---
paper_title: Posture estimation using structure and motion models

paper_content:

Sensing of human motion is very important for human-computer interactive applications such as virtual reality, gesture recognition, and communication. A vision system is suitable for human-computer interaction since this involves passive sensing and the system can estimate the motion of the user without any discomfort for the user. In this paper, we propose an algorithm for fast posture estimation of the user from an image sequence by using the position information of the hands and head. Our algorithm is founded on a model based method. The parameters of a geometric model are calculated from human kinematics. It is inadequate to obtain a unique solution from the image information such as position of the head and hands. The unknown parameters are predicted by the motion models and the previous posture parameters, then the unknown parameters are adjusted by a minimization method.
---
paper_title: Analyzing and recognizing walking figures in XYT

paper_content:

We describe a novel algorithm for gait analysis. A person walking frontoparallel to the image plane generates a characteristic "braided" pattern in a spatiotemporal (XYT) volume. Our algorithm detects this pattern, and fits it with a set of spatiotemporal snakes. The snakes can be used to find the bounding contours of the walker. The contours vary over time in a manner characteristic of each walker. Individual gaits can be recognized by applying standard pattern recognition techniques to the contour signals. >
---
paper_title: Fuzzy Qualitative Trigonometry

paper_content:

This paper proposes fuzzy qualitative representation of trigonometry (FQT) in order to bridge the gap between qualitative and quantitative representation of physical systems using trigonometry. Fuzzy qualitative coordinates are defined by replacing a unit circle with a fuzzy qualitative circle; the Cartesian translation and orientation are replaced by their fuzzy membership functions. Trigonometric functions, rules and the extensions to triangles in Euclidean space are converted into their counterparts in fuzzy qualitative coordinates using fuzzy logic and qualitative reasoning techniques. We developed a MATLAB toolbox XTrig in terms of 4-tuple fuzzy numbers to demonstrate the characteristics of the FQT. This approach addresses a representation transformation interface to connect qualitative and quantitative descriptions of trigonometry-related systems (e.g., robotic systems)
---
paper_title: Fuzzy Voxel Object

paper_content:

In this paper, computer vision and fuzzy set theory are merged for the robust construction of three-dimensional objects using a small number of cameras and minimal a priori knowledge about the objects. This work extends our previously defined crisp model, which has been successfully used for recognizing and linguistically summarizing human activity. The objects true features more closely resemble the fuzzy object than those of the crisp object. This is demonstrated both empirically and through the comparison of features used in tracking human activity. Keywords—computer vision, human activity analysis, fuzzy objects, fuzzy voxel person.
---
paper_title: Cardboard people: a parameterized model of articulated image motion

paper_content:

We extend the work of Black and Yacoob (1995) on the tracking and recognition of human facial expressions using parametrized models of optical flow to deal with the articulated motion of human limbs. We define a "card-board person model" in which a person's limbs are represented by a set of connected planar patches. The parametrized image motion of these patches in constrained to enforce articulated motion and is solved for directly using a robust estimation technique. The recovered motion parameters provide a rich and concise description of the activity that can be used for recognition. We propose a method for performing view-based recognition of human activities from the optical flow parameters that extends previous methods to cope with the cyclical nature of human motion. We illustrate the method with examples of tracking human legs of long image sequences.
---
paper_title: Fuzzy qualitative simulation

paper_content:

An approach is described that utilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, providing three significant advantages over existing techniques. Firstly, it allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The adoption of fuzzy sets also allows common-sense knowledge to be represented in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and reasoned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both strength and sign information can be represented by fuzzy relations holding against two or multivariables. Thirdly, the quantity space allows ordering information on rates of change to be used to compute temporal durations of the state and the possible transitions. Thus, an ordering of the evolution of the states and the associated temporal durations are obtained. This knowledge is used to develop an effective temporal filter that significantly reduces the number of spurious behaviors. >
---
paper_title: Local and Global Skeleton Fitting Techniques for Optical Motion Capture , Modeling and Motion Capture Techniques for Virtual Environments

paper_content:

Reference CVLAB-CONF-1998-008 URL: http://cvlab.epfl.ch/publications/publications/1998/SilaghiRRPD98.pdf Record created on 2006-02-03, modified on 2017-05-12
---
paper_title: Linguistic summarization of video for fall detection using voxel person and fuzzy logic

paper_content:

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person's states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.
---
paper_title: Recent advances in fuzzy qualitative reasoning

paper_content:

A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.
---
paper_title: Twist Based Acquisition and Tracking of Animal and Human Kinematics

paper_content:

This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use and integration of a mathematical technique, the product of exponential maps and twist motions, into a differential motion estimation. This results in solving simple linear systems, and enables us to recover robustly the kinematic degrees-of-freedom in noise and complex self occluded configurations. A new factorization technique lets us also recover the kinematic chain model itself. We are able to track several human walk cycles, several wallaby hop cycles, and two walk cycels of the famous movements of Eadweard Muybridge's motion studies from the last century. To the best of our knowledge, this is the first computer vision based system that is able to process such challenging footage.
---
paper_title: Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection

paper_content:

We present a new method for the 3D model-based tracking of human body parts. To mitigate the difficulties arising due to occlusion among body parts, we employ multiple calibrated cameras in a mutually orthogonal configuration. In addition, we develop criteria for a time varying active selection of a set of cameras to track the motion of a particular human part. In particular, at every frame, each camera tracks a number of parts depending on the visibility of these parts and the observability of their predicted motion from the specific camera. To relate points on the occluding contours of the parts to points on their models we apply concepts from projective geometry. Then, within the physics-based framework we compute the generalized forces applied from the parts' occluding contours to model points of the body parts. These forces update the translational and rotational degrees of freedom of the model, such as to minimize the discrepancy between the sensory data and the estimated model state. We present initial tracking results from a series of experiments involving the recovery of complex 3D motions in the presence of significant occlusion.
---
paper_title: Model-based tracking of self-occluding articulated objects

paper_content:

Computer sensing of hand and limb motion is an important problem for applications in human computer interaction and computer graphics. We describe a framework for local trading of self occluding motion, in which one part of an object obstructs the visibility of another. Our approach uses a kinematic model to predict occlusions and windowed templates to track partially occluded objects. We present offline 3D tracking results for hand motion with significant self occlusion. >
---
paper_title: A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Quantitative Representations

paper_content:

This paper proposes a novel framework for describing articulated robot kinematics motion with the goal of providing a unified representation by combining symbolic or qualitative functions and numerical sensing and control tasks in the context of intelligent robotics. First, fuzzy qualitative robot kinematics that provides theoretical preliminaries for the proposed robot motion representation is revisited. Second, a fuzzy qualitative framework based on clustering techniques is presented to connect numerical and symbolic robot representations. Built on the k-\bb AGOP operator (an extension of the ordered weighted aggregation operators), k-means and Gaussian functions are adapted to model a multimodal density of fuzzy qualitative kinematics parameters of a robot in both Cartesian and joint spaces; on the other hand, a mixture regressor and interpolation method are employed to convert Gaussian symbols into numerical values. Finally, simulation results in a PUMA 560 robot demonstrated that the proposed method effectively provides a two-way connection for robot representations used for both numerical and symbolic robotic tasks.
---
paper_title: A fuzzy qualitative approach to human motion recognition

paper_content:

The understanding of human motions captured in image sequences pose two main difficulties which are often regarded as computationally ill-defined: 1) modelling the uncertainty in the training data, and 2) constructing a generic activity representation that can describe simple actions as well as complicated tasks that are performed by different humans. In this paper, these problems are addressed from a direction which utilises the concept of fuzzy qualitative robot kinematics. First of all, the training data representing a typical activity is acquired by tracking the human anatomical landmarks in an image sequences. Then, the uncertainty arise when the limitations of the tracking algorithm are handled by transforming the continuous training data into a set of discrete symbolic representations - qualitative states in a quantisation process. Finally, in order to construct a template that is regarded as a combination ordered sequence of all body segments movements, robot kinematics, a well-defined solution to describe the resulting motion of rigid bodies that form the robot, has been employed. We defined these activity templates as qualitative normalised templates, a manifold trajectory of unique state transition patterns in the quantity space. Experimental results and a comparison with the hidden Markov models have demonstrated that the proposed method is very encouraging and shown a better successful recognition rate on the two available motion databases.
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paper_title: Fuzzy Qualitative Robot Kinematics

paper_content:

We propose a fuzzy qualitative (FQ) version of robot kinematics with the goal of bridging the gap between symbolic or qualitative functions and numerical sensing and control tasks for intelligent robotics. First, we revisit FQ trigonometry, and then derive its derivative extension. Next, we replace the trigonometry role in robot kinematics using FQ trigonometry and the proposed derivative extension, which leads to a FQ version of robot kinematics. FQ transformation, position, and velocity of a serial kinematics robot are derived and discussed. Finally, we propose an aggregation operator to extract robot behaviors with the highlight of the impact of the proposed methods to intelligent robotics. The proposed methods have been integrated into XTRIG MATLAB toolbox and a case study on a PUMA robot has been implemented to demonstrate their effectiveness.
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paper_title: Tracking of persons in monocular image sequences

paper_content:

We present an approach to extract quantitative geometric descriptions of the movements of persons by fitting the projection of a 3-dimensional human model to consecutive frames of an image sequence. The kinematics of the human model is given by a homogeneous transformation tree and its body parts are modeled by right-elliptical cones. The proposed approach can determine the values of a varying number of degrees of freedom (DOFs; body joints, position and orientation of the person relative to the camera) according to the application and the kind of image sequence. The determination of the DOFs is understood as an estimation problem which is solved by an iterated extended Kalman filter (IEKF). For this purpose, the human model is augmented by a simple motion model of constant velocity for all DOFs which is used in the prediction step of the IEKF. In the update step both region and edge information is used.
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paper_title: Modeling Human Activity From Voxel Person Using Fuzzy Logic

paper_content:

As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.
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paper_title: Fuzzy Qualitative Human Motion Analysis

paper_content:

This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.
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paper_title: Towards model-based recognition of human movements in image sequences

paper_content:

The interpretation of the movements of articulated bodies in image sequences is one of the most challenging problems in computer vision. In this contribution, we introduce a model-based approach for the recognition of pedestrians. We represent the human body by a 3D-model consisting of cylinders, whereas for modelling the movement of walking we use data from medical motion studies. The estimation of model parameters in consecutive images is done by applying a Kalman filter. Experimental results are shown for synthetic as well as for real image data.
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paper_title: First Sight: A human body outline labeling system

paper_content:

First Sight, a vision system in labeling the outline of a moving human body, is proposed in this paper. The emphasis of First Sight is on the analysis of motion information gathered solely from the outline of a moving human object. Two main processes are implemented in First Sight. The first process uses a novel technique to extract the outline of a moving human body from an image sequence. The second process, which employs a new human body model, interprets the outline and produces a labeled two-dimensional human body stick figure for each frame of the image sequence. Extensive knowledge of the structure, shape, and posture of the human body is used in the model. The experimental results of applying the technique on unedited image sequences with self-occlusions and missing boundary lines are encouraging. >
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paper_title: Posture estimation using structure and motion models

paper_content:

Sensing of human motion is very important for human-computer interactive applications such as virtual reality, gesture recognition, and communication. A vision system is suitable for human-computer interaction since this involves passive sensing and the system can estimate the motion of the user without any discomfort for the user. In this paper, we propose an algorithm for fast posture estimation of the user from an image sequence by using the position information of the hands and head. Our algorithm is founded on a model based method. The parameters of a geometric model are calculated from human kinematics. It is inadequate to obtain a unique solution from the image information such as position of the head and hands. The unknown parameters are predicted by the motion models and the previous posture parameters, then the unknown parameters are adjusted by a minimization method.
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paper_title: Analyzing and recognizing walking figures in XYT

paper_content:

We describe a novel algorithm for gait analysis. A person walking frontoparallel to the image plane generates a characteristic "braided" pattern in a spatiotemporal (XYT) volume. Our algorithm detects this pattern, and fits it with a set of spatiotemporal snakes. The snakes can be used to find the bounding contours of the walker. The contours vary over time in a manner characteristic of each walker. Individual gaits can be recognized by applying standard pattern recognition techniques to the contour signals. >
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paper_title: Fuzzy Voxel Object

paper_content:

In this paper, computer vision and fuzzy set theory are merged for the robust construction of three-dimensional objects using a small number of cameras and minimal a priori knowledge about the objects. This work extends our previously defined crisp model, which has been successfully used for recognizing and linguistically summarizing human activity. The objects true features more closely resemble the fuzzy object than those of the crisp object. This is demonstrated both empirically and through the comparison of features used in tracking human activity. Keywords—computer vision, human activity analysis, fuzzy objects, fuzzy voxel person.
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paper_title: Fuzzy region assignment for visual tracking

paper_content:

In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects’ features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.
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paper_title: Linguistic summarization of video for fall detection using voxel person and fuzzy logic

paper_content:

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person's states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.
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paper_title: Robust object tracking with fuzzy shape estimation

paper_content:

This work presents a novel, efficient and robust approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets with the set of image regions (blobs) extracted from each frame. Several numeric heuristics, describing the quality of gated groups of blobs and predicted tracks, are considered to generate confidence levels used in the update process. Rules are aimed to generate the most appropriate decisions under different conditions, emulating the reasoned decisions taken by an expert, and have been derived with a systematic analysis of performance. The application area is the Surveillance of airport surface, including situations with very closely spaced objects (aircraft and surface vehicles moving on apron). System performance with real image sequences of representative ground operations is shown at the end.
---
paper_title: Modeling Human Activity From Voxel Person Using Fuzzy Logic

paper_content:

As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.
---
paper_title: Fuzzy Kalman filtering

paper_content:

The classical Kalman filtering (KF) algorithm has recently been extended to interval linear systems with interval parameters under the same statistical assumptions on noise, where the new algorithm is called Interval Kalman Filtering (IKF) scheme. The IKF algorithm has the same structure, and preserves the same optimality, as the classical KF scheme but provides interval-valued estimates. If the interval system has confidence description about the distribution of its interval values, we can further incorporate the IKF scheme with fuzzy logic inference, so as to develop a new filtering algorithm, called Fuzzy Kalman Filtering (FKF) algorithm. This algorithm preserves the same recursive mechanism of the KF and IKF, but produces a scalar-valued (rather than an interval-valued) estimate at each iteration of the filtering process. To compare the FKF to the IKF, computer simulation is included, which shows that the FKF is also robust against system parameter variations.
---
paper_title: Fuzzy Voxel Object

paper_content:

In this paper, computer vision and fuzzy set theory are merged for the robust construction of three-dimensional objects using a small number of cameras and minimal a priori knowledge about the objects. This work extends our previously defined crisp model, which has been successfully used for recognizing and linguistically summarizing human activity. The objects true features more closely resemble the fuzzy object than those of the crisp object. This is demonstrated both empirically and through the comparison of features used in tracking human activity. Keywords—computer vision, human activity analysis, fuzzy objects, fuzzy voxel person.
---
paper_title: Fuzzy region assignment for visual tracking

paper_content:

In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects’ features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.
---
paper_title: The Visual Analysis of Human Movement: A Survey

paper_content:

The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people” is currently one of the most active application domains in computer vision. This survey identifies a number of promising applications and provides an overview of recent developments in this domain. The scope of this survey is limited to work on whole-body or hand motion; it does not include work on human faces. The emphasis is on discussing the various methodologies; they are grouped in 2-D approaches with or without explicit shape models and 3-D approaches. Where appropriate, systems are reviewed. We conclude with some thoughts about future directions.
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paper_title: Robust object tracking with fuzzy shape estimation

paper_content:

This work presents a novel, efficient and robust approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets with the set of image regions (blobs) extracted from each frame. Several numeric heuristics, describing the quality of gated groups of blobs and predicted tracks, are considered to generate confidence levels used in the update process. Rules are aimed to generate the most appropriate decisions under different conditions, emulating the reasoned decisions taken by an expert, and have been derived with a systematic analysis of performance. The application area is the Surveillance of airport surface, including situations with very closely spaced objects (aircraft and surface vehicles moving on apron). System performance with real image sequences of representative ground operations is shown at the end.
---
paper_title: Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique

paper_content:

The ability to determine an accurate global position of a vehicle has many useful commercial and military applications. The differential global positioning system (DGPS) is one of the practical navigation tools used for this purpose. However, the DGPS has limitations arising from slow updates, signal interference, and limited accuracy. This paper describes how vehicle rate sensors ran be used to help a DGPS overcome these limitations. The theoretical background for the sensor fusion is based on the principle of Kalman filtering and a fuzzy logic scheme. Validity of the method was verified by using experimental data from an actual automobile navigating around an urban area. The results demonstrated that the path of the automobile can be continuously traced with high accuracy and repeatability, in spite of the limitations of the DGPS.
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paper_title: Intelligent visual surveillance — A survey

paper_content:

Detection, tracking, and understanding of moving objects of interest in dynamic scenes have been active research areas in computer vision over the past decades. Intelligent visual surveillance (IVS) refers to an automated visual monitoring process that involves analysis and interpretation of object behaviors, as well as object detection and tracking, to understand the visual events of the scene. Main tasks of IVS include scene interpretation and wide area surveillance control. Scene interpretation aims at detecting and tracking moving objects in an image sequence and understanding their behaviors. In wide area surveillance control task, multiple cameras or agents are controlled in a cooperative manner to monitor tagged objects in motion. This paper reviews recent advances and future research directions of these tasks. This article consists of two parts: The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding. The second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems.
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paper_title: Human motion analysis: A review

paper_content:

Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man?machine interfaces, content-based image storage and retrieval, and video conferencing. This paper gives an overview of the various tasks involved in motion analysis of the human body. We focus on three major areas related to interpreting human motion: (1) motion analysis involving human body parts, (2) tracking a moving human from a single view or multiple camera perspectives, and (3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion from a single view or multiple perspectives focuses on higher-level processing, in which moving humans are tracked without identifying their body parts. After successfully matching the moving human image from one frame to another in an image sequence, understanding the human movements or activities comes naturally, which leads to our discussion of recognizing human activities.
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paper_title: Fuzzy Kalman filtering

paper_content:

The classical Kalman filtering (KF) algorithm has recently been extended to interval linear systems with interval parameters under the same statistical assumptions on noise, where the new algorithm is called Interval Kalman Filtering (IKF) scheme. The IKF algorithm has the same structure, and preserves the same optimality, as the classical KF scheme but provides interval-valued estimates. If the interval system has confidence description about the distribution of its interval values, we can further incorporate the IKF scheme with fuzzy logic inference, so as to develop a new filtering algorithm, called Fuzzy Kalman Filtering (FKF) algorithm. This algorithm preserves the same recursive mechanism of the KF and IKF, but produces a scalar-valued (rather than an interval-valued) estimate at each iteration of the filtering process. To compare the FKF to the IKF, computer simulation is included, which shows that the FKF is also robust against system parameter variations.
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paper_title: Fuzzy adaptive Kalman filtering for INS/GPS data fusion

paper_content:

Presents a method for sensor fusion based on adaptive fuzzy Kalman filtering. The method is applied in fusing position signals from Global Positioning Systems (GPS) and inertial navigation systems (INS) for autonomous mobile vehicles. The presented method has been validated in a 3-D environment and is of particular importance for guidance, navigation, and control of flying vehicles. The extended Kalman filter (EKF) and the noise characteristics are modified using the fuzzy logic adaptive system, and compared with the performance of a regular EKF. It is demonstrated that the fuzzy adaptive Kalman filter gives better results, in terms of accuracy, than the EKF.
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paper_title: Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system

paper_content:

Autonomous systems for surveillance, security, patrol, search and rescue are the focal point of extensive research and interest from defense and the security related industry, traffic control and other institutions. A range of sensors can be used to detect and track objects, but optical cameras or camcorders are often considered due to their convenience and passive nature. Tracking based on color intensity information is often preferred than the motion cues due to being more robust. The technique presented in this paper can also be used in conjunction with infra-red cameras, 3D lasers which result in a grey scale image. Novelty detection and tracking are two of the key elements of such systems. Most of the currently reported techniques are characterized by high computational, memory storage costs and are not autonomous because they usually require a human operator in the loop. This paper presents new approaches to both the problem of novelty detection and object tracking in video streams. These approaches are rooted in the recursive techniques that are computationally efficient and therefore potentially applicable in real-time. A novel approach for recursive density estimation (RDE) using a Cauchy type of kernel (as opposed to the usually used Gaussian one) is proposed for visual novelty detection and the use of the recently introduced evolving Takagi-Sugeno (eTS) neuro-fuzzy system for tracking the object detected by the RDE approach is proposed as opposed to the usually used Kalman filter (KF). In fact, eTS can be seen as a fuzzily weighted mixture of KF. The proposed technique is significantly faster than the well known kernel density estimation (KDE) approach for background subtraction for novelty detection and is more precise than the usually used KF. Additionally the overall approach removes the need of manually selecting the object to be tracked which makes possible a fully autonomous system for novelty detection and tracking to be developed.
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paper_title: Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle

paper_content:

Presents a method of sensor fusion based on adaptive fuzzy Kalman filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and inertial navigation system (INS) for autonomous mobile vehicles. The presented method has been validated in a 3-D environment and is of particular importance for guidance, navigation, and control of flying vehicles. The extended Kalman filter (EKF) and the noise characteristic have been modified using a fuzzy logic adaptive system and compared with the performance of the regular EKF. It has been demonstrated that the fuzzy adaptive Kalman filter gives better results (more accurate) than the EKF.
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paper_title: Nonlinear State Estimation Using Fuzzy Kalman Filter

paper_content:

In this paper, the authors have presented an approach for designing a nonlinear observer to estimate the states of a noisy dynamic system. The nonlinear observer design procedure involves representation of the nonlinear system as a family of local linear state space models; the state estimator for each linear local state space model uses standard Kalman filter theory and then a global state estimator is developed that combines the local state estimators. The effectiveness of the proposed fuzzy Kalman filter (nonlinear observer) has been demonstrated on a continuously stirred tank reactor (CSTR) process. The performances of the fuzzy Kalman filter (FKF) and the extended Kalman filter (EKF) have been compared in the presence of initial model/plant mismatch and input and output disturbances. Simulation studies also include an estimation of reactor concentration (inferential measurement), based only on the measured variable temperature of the reactor.
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paper_title: An extended Kalman filter for quaternion-based orientation estimation using MARG sensors

paper_content:

Presents an extended Kalman filter for real-time estimation of rigid body orientation using the newly developed MARG (magnetic, angular rate, and gravity) sensors. Each MARG sensor contains a three-axis magnetometer, a three-axis angular rate sensor, and a three-axis accelerometer. The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with attitude estimation. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are integrated to obtain quaternions. The Gauss-Newton iteration algorithm is utilized to find the best quaternion that relates the measured accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. The best quaternion is used as part of the measurements for the Kalman filter. As a result of this approach, the measurement equations of the Kalman filter become linear, and the computational requirements are significantly reduced, making it possible to estimate orientation in real time. Extensive testing of the filter with synthetic data and actual sensor data proved it to be satisfactory. Test cases included the presence of large initial errors as well as high noise levels. In all cases the filter was able to converge and accurately track rotational motions.
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paper_title: Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models

paper_content:

This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - a computationally efficient procedure for on-line learning TS type fuzzy models. It combines the concept of the scatter as a measure of data density and summarization ability of the TS rules, the use of Cauchy type antecedent membership functions, an aging indicator characterizing the stationarity of the rules, and a recursive least square algorithm to dynamically learn the structure and parameters of the eTS model
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paper_title: Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection

paper_content:

We present a new method for the 3D model-based tracking of human body parts. To mitigate the difficulties arising due to occlusion among body parts, we employ multiple calibrated cameras in a mutually orthogonal configuration. In addition, we develop criteria for a time varying active selection of a set of cameras to track the motion of a particular human part. In particular, at every frame, each camera tracks a number of parts depending on the visibility of these parts and the observability of their predicted motion from the specific camera. To relate points on the occluding contours of the parts to points on their models we apply concepts from projective geometry. Then, within the physics-based framework we compute the generalized forces applied from the parts' occluding contours to model points of the body parts. These forces update the translational and rotational degrees of freedom of the model, such as to minimize the discrepancy between the sensory data and the estimated model state. We present initial tracking results from a series of experiments involving the recovery of complex 3D motions in the presence of significant occlusion.
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paper_title: Implementation and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking

paper_content:

A human body motion tracking system based on use of the MARG (Magnetic, Angular Rate, and Gravity) sensors has been under development at the Naval Postgraduate School and Miami University. The design of a quaternion-based Kalman filter for processing the MARG sensor data was described in [1]. This paper presents the real-time implementation and testing results of the quaternion-based Kalman filter. Experimental results validate the Kalman filter design, and show the feasibility of the MARG sensors for real-time human body motion tracking.
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paper_title: HISTORY: The Use of the Kalman Filter for Human Motion Tracking in Virtual Reality

paper_content:

In 1960 Rudolph E. Kalman published his now famous article describing a recursive solution to the discrete-data linear filtering problem (Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME---Journal of Basic Engineering, 82 (D), 35--45, 1960). Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and applications, particularly in the area of autonomous or assisted navigation. The purpose of this paper is to acknowledge the approaching 50th anniversary of the Kalman filter with a look back at the use of the filter for human motion tracking in virtual reality (VR) and augmented reality (AR). ::: ::: In recent years there has been an explosion in the use of the Kalman filter in VR/AR. In fact, at technical conferences related to VR these days, it would be unusual to see a paper on tracking that did not use some form of a Kalman filter, or draw comparisons to those that do. As such, rather than attempt a comprehensive survey of all uses of the Kalman filter to date, what follows focuses primarily on the early discovery and subsequent period of evolution of the Kalman filter in VR, along with a few examples of modern commercial systems that use the Kalman filter. ::: ::: This paper begins with a very brief introduction to the Kalman filter, a brief look at the origins of VR, a little about tracking in VR---in particular the work and conditions that gave rise to the use of the filter, and then the evolution of the use of the filter in VR.
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paper_title: Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique

paper_content:

The ability to determine an accurate global position of a vehicle has many useful commercial and military applications. The differential global positioning system (DGPS) is one of the practical navigation tools used for this purpose. However, the DGPS has limitations arising from slow updates, signal interference, and limited accuracy. This paper describes how vehicle rate sensors ran be used to help a DGPS overcome these limitations. The theoretical background for the sensor fusion is based on the principle of Kalman filtering and a fuzzy logic scheme. Validity of the method was verified by using experimental data from an actual automobile navigating around an urban area. The results demonstrated that the path of the automobile can be continuously traced with high accuracy and repeatability, in spite of the limitations of the DGPS.
---
paper_title: An approach to online identification of Takagi-Sugeno fuzzy models

paper_content:

An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.
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paper_title: Sensor fusion based on fuzzy Kalman filter

paper_content:

We present a fuzzy Kalman filter, which is based on fuzzy logic theory and a Kalman filter. It is similar to a Kalman filter when a linear system with Gaussian noise is considered. However, when non-Gaussian noise is introduced, it is shown that the fuzzy Kalman filter outperforms the Kalman filter and the Kalman filter does not work well. We demonstrate the performances of the Kalman filter and the fuzzy Kalman filter for a position estimation application under different circumstances. Comparisons are made to draw conclusions.
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paper_title: An Introduction to the Kalman Filter

paper_content:

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman filter, a derivation, description and some discussion of the extended Kalman filter, and a relatively simple (tangible) example with real numbers & results.
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paper_title: Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking

paper_content:

Real-time tracking of human body motion is an important technology in synthetic environments, robotics, and other human-computer interaction applications. This paper presents an extended Kalman filter designed for real-time estimation of the orientation of human limb segments. The filter processes data from small inertial/magnetic sensor modules containing triaxial angular rate sensors, accelerometers, and magnetometers. The filter represents rotation using quaternions rather than Euler angles or axis/angle pairs. Preprocessing of the acceleration and magnetometer measurements using the Quest algorithm produces a computed quaternion input for the filter. This preprocessing reduces the dimension of the state vector and makes the measurement equations linear. Real-time implementation and testing results of the quaternion-based Kalman filter are presented. Experimental results validate the filter design, and show the feasibility of using inertial/magnetic sensor modules for real-time human body motion tracking
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paper_title: Fuzzy Qualitative Trigonometry

paper_content:

This paper proposes fuzzy qualitative representation of trigonometry (FQT) in order to bridge the gap between qualitative and quantitative representation of physical systems using trigonometry. Fuzzy qualitative coordinates are defined by replacing a unit circle with a fuzzy qualitative circle; the Cartesian translation and orientation are replaced by their fuzzy membership functions. Trigonometric functions, rules and the extensions to triangles in Euclidean space are converted into their counterparts in fuzzy qualitative coordinates using fuzzy logic and qualitative reasoning techniques. We developed a MATLAB toolbox XTrig in terms of 4-tuple fuzzy numbers to demonstrate the characteristics of the FQT. This approach addresses a representation transformation interface to connect qualitative and quantitative descriptions of trigonometry-related systems (e.g., robotic systems)
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paper_title: Fuzzy logic based particle filter for tracking a maneuverable target

paper_content:

In this paper we propose a new fuzzy logic-based particle filter (FLPF) algorithm for tracking a maneuvering target. The nonlinear system which is comprised of two-input and single-output are presented by fuzzy relational equations. Each of the fuzzy relational equations is expressed in a canonical-rule based form. The dynamics of the maneuvering target are modeled by multiple switching (jump Markov) systems. We assume that the target follows one-out-of-three dynamic behavior model at any time in the observation period: constant velocity (CV) motion model, clockwise coordinated turn (CCT) model, and anticlockwise coordinated turn (ACT) model. The time-varying deviation between actual and predicted positions is inferred by a two-input single-output fuzzy relation. An example is included for visualizing the effectiveness of the proposed algorithm. For comparison purposes, we simulated a conventional sequential importance sampling (SIS) algorithm. Simulation results showed that the FLPF has better tracking performance compared to the SIS.
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paper_title: Object tracking from image sequences using adaptive models in fuzzy particle filter

paper_content:

This paper describes a vision-based system for tracking objects from image sequences. The proposed system has the standard architecture with a particle filter which is a popular algorithm to track objects in real time. Many tracking algorithms have a great difficulty in tracking objects robustly by reason of complex background and rapid changes under a real complex environment such as a traffic road. To make a robust algorithm for object tracking, we propose the method that uses the adaptive autoregressive model as a state transition model and the adaptive appearance mixture model as an observation model. But, in case of changing the state of a tracked object suddenly, the adaptive models may not make the optimal parameters for accurate states at current time. Because the noise variance of the adaptive models in this case is larger than that in normal case, it has an effect on the accuracy of an object tracking algorithm. Thus, we propose a fuzzy particle filter to overcome problems from the occurrence of the unexpected improper variances due to several causes. In this paper, as the process noises and the observation noises in a fuzzy particle filter are considered as fuzzy variables by using the possibility theory, a fuzzy particle filter with fuzzy noises is used to manage uncertainty in various noise models. Also, we make possibility measure as using the fuzzy relation equation which is defined by these fuzzy variables. And then, the states are estimated by using a fuzzy expected value operator. Also, because the proposed algorithm applies several functions to improve the accuracy of tracking an object, the performance of tracking speed deteriorates. To resolve this problem to some extent, we consider the fact that a fuzzy particle filter has a little bit of an effect on the number of particles. Consequently, we propose the method which can adjust the number of particles by using the result from a measurement step in order to improve the speed for an object tracking in the proposed algorithm. The experiments of this paper show that the proposed method is efficient and has many advantages for an object tracking in real environments.
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paper_title: High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field

paper_content:

A time efficient technique for real-time tracking of high-speed objects in a video sequence is presented in this article. The technique is primarily based on the segmentation of the optical flow field computed between the successive image frames of a video sequence, followed by the tracking of a detected point of interest (POI) within the segmented flow field. In the initial phase of the technique, the optical flow field between the first two successive image frames acquired from a video sequence, is computed. A fuzzy hostility index indicative of the degree of coherence of the moving objects in the image frames, is used to segment the optical flow field. This yields different coherent regions of interest (ROIs) in the segmented flow field. A POI is then detected in the different ROIs obtained. Tracking of the moving object is then carried out by computing the flow fields between predefined ROIs in the neighborhood of the detected POI in the subsequent image frames. Since the selected ROIs are smaller than the image frames, a fair amount of reduction in the time required for the computation of the optical flow field is achieved, thereby facilitating real-time operation. An application of the proposed technique is demonstrated on three video sequences of high-speed flying fighter aircrafts.
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paper_title: Target Tracking Using Fuzzy Hostility Induced Segmentation of Optical Flow Field

paper_content:

An interesting application of the soft computing paradigm involving fuzzy set-theoretic concepts is the tracking of targets from a motion scene preceded by faithful extraction of motion object features and prototypes. Tracking is essentially a three step process. The first step is involved in analyzing the motion scene under consideration for extracting moving object-centric features. Subsequently, the selected features are used to segment/cluster the motion scene into several moving object regions. Finally, the feature space is updated based on the analysis of the segmented motion scene for future retrieval of moving object regions.
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paper_title: A fuzzy qualitative approach to human motion recognition

paper_content:

The understanding of human motions captured in image sequences pose two main difficulties which are often regarded as computationally ill-defined: 1) modelling the uncertainty in the training data, and 2) constructing a generic activity representation that can describe simple actions as well as complicated tasks that are performed by different humans. In this paper, these problems are addressed from a direction which utilises the concept of fuzzy qualitative robot kinematics. First of all, the training data representing a typical activity is acquired by tracking the human anatomical landmarks in an image sequences. Then, the uncertainty arise when the limitations of the tracking algorithm are handled by transforming the continuous training data into a set of discrete symbolic representations - qualitative states in a quantisation process. Finally, in order to construct a template that is regarded as a combination ordered sequence of all body segments movements, robot kinematics, a well-defined solution to describe the resulting motion of rigid bodies that form the robot, has been employed. We defined these activity templates as qualitative normalised templates, a manifold trajectory of unique state transition patterns in the quantity space. Experimental results and a comparison with the hidden Markov models have demonstrated that the proposed method is very encouraging and shown a better successful recognition rate on the two available motion databases.
---
paper_title: Fuzzy Qualitative Human Motion Analysis

paper_content:

This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.
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paper_title: Fuzzy Particle Filtering for Uncertain Systems

paper_content:

In this paper, we propose a novel fuzzy particle filtering method for online estimation of nonlinear dynamic systems with fuzzy uncertainties. This approach uses a sequential fuzzy simulation to approximate the possibilities of the state intervals in the state-space, and estimates the state by fuzzy expected value operator. To solve the degeneracy problem of the fuzzy particle filter, one corresponding resampling technique is introduced. In addition, we compare the fuzzy particle filter with ordinary particle filter in both aspects of the theoretical basis and algorithm design, and demonstrate that the proposed filter outperforms standard particle filters especially when the number of the particles is small. The numerical simulations of two continuous-state nonlinear systems and a jump Markov system are employed to show the effectiveness and robustness of the proposed fuzzy particle filter.
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paper_title: Binary object extraction using bi-directional self-organizing neural network (BDSONN) architecture with fuzzy context sensitive thresholding

paper_content:

A novel neural network architecture suitable for image processing applications and comprising three interconnected fuzzy layers of neurons and devoid of any back-propagation algorithm for weight adjustment is proposed in this article. The fuzzy layers of neurons represent the fuzzy membership information of the image scene to be processed. One of the fuzzy layers of neurons acts as an input layer of the network. The two remaining layers viz. the intermediate layer and the output layer are counter-propagating fuzzy layers of neurons. These layers are meant for processing the input image information available from the input layer. The constituent neurons within each layer of the network architecture are fully connected to each other. The intermediate layer neurons are also connected to the corresponding neurons and to a set of neighbors in the input layer. The neurons at the intermediate layer and the output layer are also connected to each other and to the respective neighbors of the corresponding other layer following a neighborhood based connectivity. The proposed architecture uses fuzzy membership based weight assignment and subsequent updating procedure. Some fuzzy cardinality based image context sensitive information are used for deciding the thresholding capabilities of the network. The network self organizes the input image information by counter-propagation of the fuzzy network states between the intermediate and the output layers of the network. The attainment of stability of the fuzzy neighborhood hostility measures at the output layer of the network or the corresponding fuzzy entropy measures determine the convergence of the network operation. An application of the proposed architecture for the extraction of binary objects from various degrees of noisy backgrounds is demonstrated using a synthetic and a real life image.
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paper_title: The computation of optical flow

paper_content:

Two-dimensional image motion is the projection of the three-dimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of time-orderedimages allow the estimation of projected two-dimensional image motion as either instantaneous image velocities or discrete image displacements. These are usually called the optical flow field or the image velocity field . Provided that optical flow is a reliable approximation to two-dimensional image motion, it may then be used to recover the three-dimensional motion of the visual sensor (to within a scale factor) and the three-dimensional surface structure (shape or relative depth) through assumptions concerning the structure of the optical flow field, the three-dimensional environment, and the motion of the sensor. Optical flow may also be used to perform motion detection, object segmentation, time-to-collision and focus of expansion calculations, motion compensated encoding, and stereo disparity measurement. We investigate the computation of optical flow in this survey: widely known methods for estimating optical flow are classified and examined by scrutinizing the hypothesis and assumptions they use. The survey concludes with a discussion of current research issues.
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paper_title: High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field

paper_content:

A time efficient technique for real-time tracking of high-speed objects in a video sequence is presented in this article. The technique is primarily based on the segmentation of the optical flow field computed between the successive image frames of a video sequence, followed by the tracking of a detected point of interest (POI) within the segmented flow field. In the initial phase of the technique, the optical flow field between the first two successive image frames acquired from a video sequence, is computed. A fuzzy hostility index indicative of the degree of coherence of the moving objects in the image frames, is used to segment the optical flow field. This yields different coherent regions of interest (ROIs) in the segmented flow field. A POI is then detected in the different ROIs obtained. Tracking of the moving object is then carried out by computing the flow fields between predefined ROIs in the neighborhood of the detected POI in the subsequent image frames. Since the selected ROIs are smaller than the image frames, a fair amount of reduction in the time required for the computation of the optical flow field is achieved, thereby facilitating real-time operation. An application of the proposed technique is demonstrated on three video sequences of high-speed flying fighter aircrafts.
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paper_title: Determining Optical Flow

paper_content:

Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantified rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.
---
paper_title: Target Tracking Using Fuzzy Hostility Induced Segmentation of Optical Flow Field

paper_content:

An interesting application of the soft computing paradigm involving fuzzy set-theoretic concepts is the tracking of targets from a motion scene preceded by faithful extraction of motion object features and prototypes. Tracking is essentially a three step process. The first step is involved in analyzing the motion scene under consideration for extracting moving object-centric features. Subsequently, the selected features are used to segment/cluster the motion scene into several moving object regions. Finally, the feature space is updated based on the analysis of the segmented motion scene for future retrieval of moving object regions.
---
paper_title: Detecting Pedestrian Abnormal Behavior Based on Fuzzy Associative Memory

paper_content:

Visual analysis of human motion in video sequences has attracted more and more attention to computer visions in recent years. In order to indicate the pedestrian movement in Intelligent Security Monitoring System, an articulated model of human is presented. According to the contour angle movement of bodypsilas major organs, a fuzzy function is designed. Fuzzy Associative Memory (FAM) is proposed to infer abnormal behavior of the walker. The overall degree of the anomaly is resulted from the fuzzy membership of the pedestrian's organ using a three layer FAM. In the realization of the system a combined method of centroid and fuzzy discriminant is presented. Fuzzy discriminant can detect irregularities and implements initiative analysis of body's behaviors in visual surveillance. Therefore, we can recognize some abnormal behaviors and then alarm. The results show that the new algorithm has better performance.
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paper_title: Cluster labeling and parameter estimation for the automated setup of a hand-gesture recognition system

paper_content:

In this work, we address the issue of reconfigurability of a hand-gesture recognition system. The calibration or setup of the operational parameters of such a system is a time-consuming effort, usually performed by trial and error, and often causing system performance to suffer because of designer impatience. In this work, we suggest a methodology using a neighborhood-search algorithm for tuning system parameters. Thus, the design of hand-gesture recognition systems is transformed into an optimization problem. To test the methodology, we address the difficult problem of simultaneous calibration of the parameters of the image processing/fuzzy C-means (FCM) components of a hand-gesture recognition system. In addition, we proffer a method for supervising the FCM algorithm using linear programming and heuristic labeling. Resulting solutions exhibited fast convergence (in the order of ten iterations) to reach recognition accuracies within several percent of the optimal. Comparative performance testing using three gesture databases (BGU, American Sign Language and Gripsee), and a real-time implementation (Tele-Gest) are reported on.
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paper_title: Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application

paper_content:

A new classification approach for human body postures based on a neural fuzzy network is proposed in this paper, and the approach is applied to detect emergencies that are caused by accidental falls. Four main body postures are used for posture classification, including standing, bending, sitting, and lying. After the human body is segmented from the background, the classification features are extracted from the silhouette. The body silhouette is projected onto horizontal and vertical axes, and then, a discrete Fourier transform is applied to each projected histogram. Magnitudes of significant Fourier transform coefficients together with the silhouette length-width ratio are used as features. The classifier is designed by a neural fuzzy network. The four postures can be classified with high accuracy according to experimental results. Classification results are also applicable to home care emergency detection of a person who suddenly falls and remains in the lying posture for a period of time due to experiments that were performed.
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paper_title: Human–Computer Interaction for Smart Environment Applications Using Fuzzy Hand Posture and Gesture Models

paper_content:

Ever since the assemblage of the first computer, efforts have been made to improve the way people could use machines. Recently, the usage of smart environments has become popular in order to make everyday living more comfortable and to improve the quality of living of humans. In this paper, a hand posture and gesture modeling and recognition system is introduced, which can be used as an interface to make possible communication with smart environment (intelligent space) by simple hand gestures. The system transforms preprocessed data of the detected hand into a fuzzy hand-posture feature model by using fuzzy neural networks and based on this model determines the actual hand posture applying fuzzy inference. Finally, from the sequence of detected hand postures, the system can recognize the hand gesture of the user.
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paper_title: Multi-prototype fuzzy clustering with fuzzy K-nearest neighbor for off-line human action recognition

paper_content:

Fall detection of elderly in home environment is an important research area. The fall detection is a part of the human action recognition. In this paper, a human action detection using the fuzzy clustering algorithm with the fuzzy K-nearest neighbor from view-invariant human motion analysis is implemented. In particular, the Hu moment invariant features are computed. Then principal component analysis is utilized to select the principal components. The fuzzy clustering algorithm (either fuzzy C-means, Gustafson and Kessel, or Gath and Geva) is implemented on each class to select the prototypes representing the class. From the results, we found that the best classification rate on the validation set is around 99.33% to 100%, and the classification rate on the blind test data set is around 90%. We also compare the result from fuzzy K-nearest neighbor with that from K-nearest neighbor. The fuzzy K-nearest neighbor result is better as expected.
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paper_title: Vision based hand gesture recognition using finite state machines and fuzzy logic

paper_content:

This paper proposes a finite state and fuzzy logic based approach to hand gesture learning and recognition. The location of 2D image positions of the hands of the user is obtained by Edge detection and vector extraction. These are used to identify the hand posture as well as the center of the hand of the user. We first learn the spatial information without data segmentation and alignment. Then the data is grouped into clusters that are associated with information for temporal alignment. The points thus obtained are clustered using Fuzzy c-mean clustering algorithm. These clusters of hand posture further determine the states of the Finite State Machine(s) through which the succeeding gesture has to be matched. To build a Gesture Recognizer (GR) the temporal information is integrated. Each hand gesture is defined to be an ordered sequence of states in spatial-temporal space i.e. FSM corresponding to it. The number of states/clusters in an FSM represents a trade-off between the accuracy of gesture recognizer and the amount of spatial-temporal data it stores.
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paper_title: Tracking non-rigid, moving objects based on color cluster flow

paper_content:

In this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. The application background is vision-based driving assistance in the inner city. In an initial step, object parts are determined by a divisive clustering algorithm, which is applied to all pixels in the first image of the sequence. The feature space is defined by the color and position of a pixel. For each new image the clusters of the previous image are adapted iteratively by a parallel k-means clustering algorithm. Instead of tracking single points, edges, or areas over a sequence of images, only the centroids of the clusters are tracked. The proposed method remarkably simplifies the correspondence problem and also ensures a robust tracking behaviour.
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paper_title: A multi-object tracking system for surveillance video analysis

paper_content:

We present a novel and robust clustering based multi-object tracking system for surveillance video analysis. It is designed to extract the trajectory data of vehicles in crowded traffic scenes and can be extended to other applications of surveillance and sports video analysis. In our system, a fast accurate fuzzy clustering algorithm is employed, and the feature space is constructed by extracting the position, color and velocity information of foreground pixels. By using growing and predictive adaptation, fixed linkages are expected between meaningful targets and corresponding active cluster centroids. In this way the motion classifier and tracker are combined seamlessly. Experimental results suggest the efficiency and robustness of the proposed method with severe occlusions and clutter effect.
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paper_title: Recognition of gestures in Arabic sign language using neuro-fuzzy systems

paper_content:

Hand gestures play an important role in communication between people during their daily lives. But the extensive use of hand gestures as a mean of communication can be found in sign languages. Sign language is the basic communication method between deaf people. A translator is usually needed when an ordinary person wants to communicate with a deaf one. The work presented in this paper aims at developing a system for automatic translation of gestures of the manual alphabets in the Arabic sign language. In doing so, we have designed a collection of ANFIS networks, each of which is trained to recognize one gesture. Our system does not rely on using any gloves or visual markings to accomplish the recognition job. Instead, it deals with images of bare hands, which allows the user to interact with the system in a natural way. An image of the hand gesture is processed and converted into a set of features that comprises of the lengths of some vectors which are selected to span the fingertips' region. The extracted features are rotation, scale, and translation invariat, which makes the system more flexible. The subtractive clustering algorithm and the least-squares estimator are used to identify the fuzzy inference system, and the training is achieved using the hybrid learning algorithm. Experiments revealed that our system was able to recognize the 30 Arabic manual alphabets with an accuracy of 93.55%.
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paper_title: Real-time hand gesture telerobotic system using fuzzy c-means clustering

paper_content:

This paper describes a teleoperation system in which an articulated robot performs a block pushing task based on hand gesture commands sent through the Internet. A fuzzy c-means clustering method is used to classify hand postures as "gesture commands". The fuzzy recognition system was tested using 20 trials each of a 12-gesture vocabulary. Results revealed an acceptance rate of 99.6% (percent of gestures with a sufficiently large membership value to belong to at least one of the designated classifications), and a recognition accuracy of 100% (the percent of accepted gestures classified correctly). Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.
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paper_title: Hand Gesture Recognition Using Fuzzy Neural Network

paper_content:

In this paper, we introduce a new approach to gesture recognition based on incorporating the idea of fuzzy ARTMAP [1] in the feature recognition neural network and Hussain and Kabuka [3]. It has already shown that neural networks are relatively effective for partially corrupted images. However, a distinct subnet is created for every training pattern. Therefore, a big network is obtained when the number training patterns is large. Furthermore, recognition rate can be hurt due to the failure of combining features from similar training patterns. We would like to improve this technique by considering input images as fuzzy resources for hand gesture recognition. Training patterns are allowed to be merged, based on the measure of similarity among features, resulting in a subnet being shared by similar patterns and network size is reduced and recognition rate is increased. The high gesture recognition rates and quick network retraining times found in the present study suggest that a neural network approach to gesture recognition be further evaluated.
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paper_title: Learning activity patterns using fuzzy self-organizing neural network

paper_content:

Activity understanding in visual surveillance has attracted much attention in recent years. In this paper, we present a new method for learning patterns of object activities in image sequences for anomaly detection and activity prediction. The activity patterns are constructed using unsupervised learning of motion trajectories and object features. Based on the learned activity patterns, anomaly detection and activity prediction can be achieved. Unlike existing neural network based methods, our method uses a whole trajectory as an input to the network. This makes the network structure much simpler. Furthermore, the fuzzy set theory based method and the batch learning method are introduced into the network learning process, and make the learning process much more efficient. Two sets of data acquired, respectively, from a model scene and a campus scene are both used to test the proposed algorithms. Experimental results show that the fuzzy self-organizing neural network (fuzzy SOM) is much more efficient than the Kohonen self-organizing feature map (SOFM) and vector quantization in both speed and accuracy, and the anomaly detection and activity prediction algorithms have encouraging performances.
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paper_title: Gesture Recognition: A Survey

paper_content:

Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted
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paper_title: Automatic classification of single facial images

paper_content:

We propose a method for automatically classifying facial images based on labeled elastic graph matching, a 2D Gabor wavelet representation, and linear discriminant analysis. Results of tests with three image sets are presented for the classification of sex, "race", and expression. A visual interpretation of the discriminant vectors is provided.
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paper_title: A Fuzzy Spatio-Temporal-based Approach for Activity Recognition

paper_content:

Over the last decade, there has been a significant deployment of systems dedicated to surveillance. These systems make use of real-time sensors that generate continuous streams of data. Despite their success in many cases, the increased number of sensors leads to a cognitive overload for the operator in charge of their analysis. However, the context and the application requires an ability to react in real-time. The research presented in this paper introduces a spatio-temporal-based approach the objective of which is to provide a qualitative interpretation of the behavior of an entity (e.g., a human or vehicle). The process is formally supported by a fuzzy logic-based approach, and designed in order to be as generic as possible.
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paper_title: A fuzzy logic-based system for the automation of human behavior recognition using machine vision in intelligent environments

paper_content:

The recent years have witnessed significant progress in the automation of human behavior recognition using machine vision in order to realize intelligent environments which are capable of detecting users' actions and gestures so that the needed services can be provided automatically and instantly for maximizing the user comfort and safety as well as minimizing energy. However, the majority of traditional human behavior machine vision-based recognition approaches rely on assumptions (such as known spatial locations and temporal segmentations) or computationally expensive approaches (such as sliding window search through a spatio-temporal volume). Hence, it is difficult for such methods to scale up and handle the high uncertainty levels and complexities available in real-world applications. This paper proposes a novel fuzzy machine vision-based framework for efficient humans' behavior recognition. A model-based feature set is utilized to extract visual feature cues including silhouette slices and movement speed from the human silhouette in video sequences which are analyzed as inputs by the proposed fuzzy system. We have employed fuzzy c-means clustering to learn the membership functions of the proposed fuzzy system. The behavior recognition was implemented via selecting the best candidate's behavior category with the highest output degree as the recognized behavior. We have successfully tested our system on the publicly available Weizmann human action dataset where our fuzzy-based system produced an average recognition accuracy of 94.03 %, which outperformed the traditional non-fuzzy systems based on hidden Markov models and other state-of-the-art approaches which were applied on the Weizmann dataset.
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paper_title: Fuzzy vector quantization algorithms and their application in image compression.

paper_content:

This paper presents the development and evaluation of fuzzy vector quantization algorithms. These algorithms are designed to achieve the quality of vector quantizers provided by sophisticated but computationally demanding approaches, while capturing the advantages of the frequently used in practice k-means algorithm, such as speed, simplicity, and conceptual appeal. The uncertainty typically associated with clustering tasks is formulated in this approach by allowing the assignment of each training vector to multiple clusters in the early stages of the iterative codebook design process. A training vector assignment strategy is also proposed for the transition from the fuzzy mode, where each training vector can be assigned to multiple clusters, to the crisp mode, where each training vector can be assigned to only one cluster. Such a strategy reduces the dependence of the resulting codebook on the random initial codebook selection. The resulting algorithms are used in image compression based on vector quantization. This application provides the basis for evaluating the computational efficiency of the proposed algorithms and comparing the quality of the resulting codebook design with that provided by competing techniques.
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paper_title: Combining Fuzzy Vector Quantization With Linear Discriminant Analysis for Continuous Human Movement Recognition

paper_content:

In this paper, a novel method for continuous human movement recognition based on fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) is proposed. We regard a movement as a unique combination of basic movement patterns, the so-called dynemes. The proposed algorithm combines FVQ and LDA to discover the most discriminative dynemes as well as represent and discriminate the different human movements in terms of these dynemes. This method allows for simple Mahalanobis or cosine distance comparison of not aligned human movements, taking into account implicitly time shifts and internal speed variations, and, thus, aiding the design of a real-time continuous human movement recognition algorithm. The effectiveness and robustness of this method is shown by experimental results on a standard dataset with videos captured under real conditions, and on a new video dataset created using motion capture data.
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paper_title: Fuzzy Qualitative Human Motion Analysis

paper_content:

This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.
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paper_title: Activity-Based Person Identification Using Fuzzy Representation and Discriminant Learning

paper_content:

In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which “walk” (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy vector quantization and linear discriminant analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition, and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.
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paper_title: A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Quantitative Representations

paper_content:

This paper proposes a novel framework for describing articulated robot kinematics motion with the goal of providing a unified representation by combining symbolic or qualitative functions and numerical sensing and control tasks in the context of intelligent robotics. First, fuzzy qualitative robot kinematics that provides theoretical preliminaries for the proposed robot motion representation is revisited. Second, a fuzzy qualitative framework based on clustering techniques is presented to connect numerical and symbolic robot representations. Built on the k-\bb AGOP operator (an extension of the ordered weighted aggregation operators), k-means and Gaussian functions are adapted to model a multimodal density of fuzzy qualitative kinematics parameters of a robot in both Cartesian and joint spaces; on the other hand, a mixture regressor and interpolation method are employed to convert Gaussian symbols into numerical values. Finally, simulation results in a PUMA 560 robot demonstrated that the proposed method effectively provides a two-way connection for robot representations used for both numerical and symbolic robotic tasks.
---
paper_title: A fuzzy qualitative approach to human motion recognition

paper_content:

The understanding of human motions captured in image sequences pose two main difficulties which are often regarded as computationally ill-defined: 1) modelling the uncertainty in the training data, and 2) constructing a generic activity representation that can describe simple actions as well as complicated tasks that are performed by different humans. In this paper, these problems are addressed from a direction which utilises the concept of fuzzy qualitative robot kinematics. First of all, the training data representing a typical activity is acquired by tracking the human anatomical landmarks in an image sequences. Then, the uncertainty arise when the limitations of the tracking algorithm are handled by transforming the continuous training data into a set of discrete symbolic representations - qualitative states in a quantisation process. Finally, in order to construct a template that is regarded as a combination ordered sequence of all body segments movements, robot kinematics, a well-defined solution to describe the resulting motion of rigid bodies that form the robot, has been employed. We defined these activity templates as qualitative normalised templates, a manifold trajectory of unique state transition patterns in the quantity space. Experimental results and a comparison with the hidden Markov models have demonstrated that the proposed method is very encouraging and shown a better successful recognition rate on the two available motion databases.
---
paper_title: Fuzzy Qualitative Robot Kinematics

paper_content:

We propose a fuzzy qualitative (FQ) version of robot kinematics with the goal of bridging the gap between symbolic or qualitative functions and numerical sensing and control tasks for intelligent robotics. First, we revisit FQ trigonometry, and then derive its derivative extension. Next, we replace the trigonometry role in robot kinematics using FQ trigonometry and the proposed derivative extension, which leads to a FQ version of robot kinematics. FQ transformation, position, and velocity of a serial kinematics robot are derived and discussed. Finally, we propose an aggregation operator to extract robot behaviors with the highlight of the impact of the proposed methods to intelligent robotics. The proposed methods have been integrated into XTRIG MATLAB toolbox and a case study on a PUMA robot has been implemented to demonstrate their effectiveness.
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paper_title: Fuzzy Qualitative Human Motion Analysis

paper_content:

This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.
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paper_title: Parametric Hidden Markov Models for Gesture Recognition

paper_content:

A method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation; one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the HMM states. Using a linear model of dependence, we formulate an expectation-maximization (EM) method for training the parametric HMM. During testing, a similar EM algorithm simultaneously maximizes the output likelihood of the PHMM for the given sequence and estimates the quantifying parameters. Using visually derived and directly measured three-dimensional hand position measurements as input, we present results that demonstrate the recognition superiority of the PHMM over standard HMM techniques, as well as greater robustness in parameter estimation with respect to noise in the input features. Finally, we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. The nonlinear formulation requires the use of a generalized expectation-maximization (GEM) algorithm for both training and the simultaneous recognition of the gesture and estimation of the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural spherical coordinate parameterization of pointing direction.
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paper_title: A Bayesian computer vision system for modeling human interactions

paper_content:

We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system deals in particularly with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. Finally, a synthetic "Alife-style" training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.
---
paper_title: Recognizing human action in time-sequential images using hidden Markov model

paper_content:

A human action recognition method based on a hidden Markov model (HMM) is proposed. It is a feature-based bottom-up approach that is characterized by its learning capability and time-scale invariability. To apply HMMs, one set of time-sequential images is transformed into an image feature vector sequence, and the sequence is converted into a symbol sequence by vector quantization. In learning human action categories, the parameters of the HMMs, one per category, are optimized so as to best describe the training sequences from the category. To recognize an observed sequence, the HMM which best matches the sequence is chosen. Experimental results for real time-sequential images of sports scenes show recognition rates higher than 90%. The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer. >
---
paper_title: A Novel Fuzzy HMM Approach for Human Action Recognition in Video

paper_content:

Hidden Markov Model (HMM) has been used in Human Action Recognition (HAR) since early 1980s. In this paper, an improvement of HMM using fuzzy concepts is proposed and used in HAR. HMM fuzzification is used widely in some research areas such as speech recognition and medicine, but it is used in HAR rarely. To increase the classification performance and decrease information losing in vector quantization, a fuzzy approach is used in HMM implementation. In feature extraction step, a human is represented by a skeletonization method which is effective and almost real-time. Experiments show that recognition rate increases about 6 percent using this approach. We call this approach Fuzzy Hidden Markov Model (Fuzzy HMM).
---
paper_title: Recognition of human body motion using phase space constraints

paper_content:

A new method for representing and recognizing human body movements is presented. The basic idea is to identify sets of constraints that are diagnostic of a movement: expressed using body-centered coordinates such as joint angles and in force only during a particular movement. Assuming the availability of Cartesian tracking data, we develop techniques for a representation of movements defined by space curves in subspaces of a "phase space." The phase space has axes of joint angles and torso location and attitude, and the axes of the subspaces are subsets of the axes of the phase space. Using this representation we develop a system for learning new movements from ground truth data by searching for constraints. We then use the learned representation for recognizing movements in unsegmented data. We train and test the system on nine fundamental steps from classical ballet performed by two dancers; the system accurately recognizes the movements in the unsegmented stream of motion. >
---
paper_title: A state-based technique for the summarization and recognition of gesture

paper_content:

We define a gesture to be a sequence of states in a measurement or configuration space. For a given gesture, these states are used to capture both the repeatability and variability evidenced in a training set of example trajectories. The states are positioned along a prototype of the gesture, and shaped such that they are narrow in the directions in which the ensemble of examples is tightly constrained, and wide in directions in which a great deal of variability is observed. We develop techniques for computing a prototype trajectory of an ensemble of trajectories, for defining configuration states along the prototype, and for recognizing gestures from an unsegmented, continuous stream of sensor data. The approach is illustrated by application to a range of gesture-related sensory data: the two-dimensional movements of a mouse input device, the movement of the hand measured by a magnetic spatial position and orientation sensor, and, lastly, the changing eigenvector projection coefficients computed from an image sequence. >
---
paper_title: Fuzzy action recognition for multiple views within single camera

paper_content:

To be able to perform human action recognition from multiple views is a great challenge in the field of computer vision. State-of-the-art solutions have been focusing on building a 3D action model from multiple views in a multi, calibrated cameras' environment. Promising results were achieved; however, these approaches tend to assume that human action is performed frontal-parallel to each of the multiple cameras. In a real world scenario, this is not always true and the overlapping regions in such systems are very limited. In this paper, we proposed a fuzzy action recognition framework for multiple views within a single camera. We adopted fuzzy quantity space in the framework and introduced a new concept called the Signature Action Behaviour to model an action from multiple views and represent it as fuzzy descriptor. Then, distance measure is applied to deduce an action. Experimental results showed the efficiency of our proposed framework in modeling the actions from different viewpoints and styles.
---
paper_title: Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis

paper_content:

In this paper, a novel multi-view human movement recognition method is presented. A novel representation of multi-view human movement videos is proposed that is based on learning basic multi-view human movement primitives, called multi-view dynemes. The movement video is represented in a new feature space (called dyneme space) using these multi-view dynemes, thus producing a time invariant multi-view movement representation. Fuzzy distances from the multi-view dynemes are used to represent the human body postures in the dyneme space. Three variants of Linear Discriminant Analysis (LDA) are evaluated to achieve a discriminant movement representation in a low dimensionality space. The view identification problem is solved either by using a circular block shift procedure followed by the evaluation of the minimum Euclidean distance from any dyneme, or by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). The discriminant movement representation combined with camera viewpoint identification and a nearest centroid classification step leads to a high human movement classification accuracy.
---
paper_title: Person specific activity recognition using fuzzy learning and Discriminant Analysis

paper_content:

One of the major issues that activity recognition methods should be able to face is the style variations observed in the execution of activities performed by different humans. In order to address this issue we propose a person-specific activity recognition framework in which human identification proceeds activity recognition. After recognizing the ID of the human depicted in a video stream, a person-specific activity classifier is responsible to recognize the activity performed by the human. Exploiting the enriched human body information captured by a multi-camera setup, view-invariant person and activity representations are obtained. The classification procedure involves Fuzzy Vector Quantization and Linear Discriminant Analysis. The proposed method is applied on drinking and eating activity recognition as well as on other activity recognition tasks. Experiments show that the person-specific approach outperforms the person-independent one.
---
paper_title: Minimum Class Variance Extreme Learning Machine for Human Action Recognition

paper_content:

In this paper, we propose a novel method aiming at view-independent human action recognition. Action description is based on local shape and motion information appearing at spatiotemporal locations of interest in a video. Action representation involves fuzzy vector quantization, while action classification is performed by a feedforward neural network. A novel classification algorithm, called minimum class variance extreme learning machine, is proposed in order to enhance the action classification performance. The proposed method can successfully operate in situations that may appear in real application scenarios, since it does not set any assumption concerning the visual scene background and the camera view angle. Experimental results on five publicly available databases, aiming at different application scenarios, denote the effectiveness of both the adopted action recognition approach and the proposed minimum class variance extreme learning machine algorithm.
---
paper_title: Fuzzy action recognition for multiple views within single camera

paper_content:

To be able to perform human action recognition from multiple views is a great challenge in the field of computer vision. State-of-the-art solutions have been focusing on building a 3D action model from multiple views in a multi, calibrated cameras' environment. Promising results were achieved; however, these approaches tend to assume that human action is performed frontal-parallel to each of the multiple cameras. In a real world scenario, this is not always true and the overlapping regions in such systems are very limited. In this paper, we proposed a fuzzy action recognition framework for multiple views within a single camera. We adopted fuzzy quantity space in the framework and introduced a new concept called the Signature Action Behaviour to model an action from multiple views and represent it as fuzzy descriptor. Then, distance measure is applied to deduce an action. Experimental results showed the efficiency of our proposed framework in modeling the actions from different viewpoints and styles.
---
paper_title: Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis

paper_content:

In this paper, we present a view-independent action recognition method exploiting a low computational-cost volumetric action representation. Binary images depicting the human body during action execution are accumulated in order to produce the so-called action volumes. A novel time-invariant action representation is obtained by exploiting the circular shift invariance property of the magnitudes of the Discrete Fourier Transform coefficients. The similarity of an action volume with representative action volumes is exploited in order to map it to a lower-dimensional feature space that preserves the action class properties. Discriminant learning is, subsequently, employed for further dimensionality reduction and action class discrimination. By using such an action representation, the proposed approach performs fast action recognition. By combining action recognition results coming from different view angles, high recognition rates are obtained. The proposed method is extended to interaction recognition, i.e., to human action recognition involving two persons. The proposed approach is evaluated on a publicly available action recognition database using experimental settings simulating situations that may appear in real-life applications, as well as on a new nutrition support action recognition database.
---
paper_title: Activity-Based Person Identification Using Fuzzy Representation and Discriminant Learning

paper_content:

In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which “walk” (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy vector quantization and linear discriminant analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition, and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.
---
paper_title: Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis

paper_content:

In this paper, a novel multi-view human movement recognition method is presented. A novel representation of multi-view human movement videos is proposed that is based on learning basic multi-view human movement primitives, called multi-view dynemes. The movement video is represented in a new feature space (called dyneme space) using these multi-view dynemes, thus producing a time invariant multi-view movement representation. Fuzzy distances from the multi-view dynemes are used to represent the human body postures in the dyneme space. Three variants of Linear Discriminant Analysis (LDA) are evaluated to achieve a discriminant movement representation in a low dimensionality space. The view identification problem is solved either by using a circular block shift procedure followed by the evaluation of the minimum Euclidean distance from any dyneme, or by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). The discriminant movement representation combined with camera viewpoint identification and a nearest centroid classification step leads to a high human movement classification accuracy.
---
paper_title: Advances in View-Invariant Human Motion Analysis: A Review

paper_content:

As viewpoint issue is becoming a bottleneck for human motion analysis and its application, in recent years, researchers have been devoted to view-invariant human motion analysis and have achieved inspiring progress. The challenge here is to find a methodology that can recognize human motion patterns to reach increasingly sophisticated levels of human behavior description. This paper provides a comprehensive survey of this significant research with the emphasis on view-invariant representation, and recognition of poses and actions. In order to help readers understand the integrated process of visual analysis of human motion, this paper presents recent development in three major issues involved in a general human motion analysis system, namely, human detection, view-invariant pose representation and estimation, and behavior understanding. Public available standard datasets are recommended. The concluding discussion assesses the progress so far, and outlines some research challenges and future directions, and solution to what is essential to achieve the goals of human motion analysis.
---
paper_title: Combining Fuzzy Vector Quantization With Linear Discriminant Analysis for Continuous Human Movement Recognition

paper_content:

In this paper, a novel method for continuous human movement recognition based on fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) is proposed. We regard a movement as a unique combination of basic movement patterns, the so-called dynemes. The proposed algorithm combines FVQ and LDA to discover the most discriminative dynemes as well as represent and discriminate the different human movements in terms of these dynemes. This method allows for simple Mahalanobis or cosine distance comparison of not aligned human movements, taking into account implicitly time shifts and internal speed variations, and, thus, aiding the design of a real-time continuous human movement recognition algorithm. The effectiveness and robustness of this method is shown by experimental results on a standard dataset with videos captured under real conditions, and on a new video dataset created using motion capture data.
---
paper_title: Minimum Class Variance Extreme Learning Machine for Human Action Recognition

paper_content:

In this paper, we propose a novel method aiming at view-independent human action recognition. Action description is based on local shape and motion information appearing at spatiotemporal locations of interest in a video. Action representation involves fuzzy vector quantization, while action classification is performed by a feedforward neural network. A novel classification algorithm, called minimum class variance extreme learning machine, is proposed in order to enhance the action classification performance. The proposed method can successfully operate in situations that may appear in real application scenarios, since it does not set any assumption concerning the visual scene background and the camera view angle. Experimental results on five publicly available databases, aiming at different application scenarios, denote the effectiveness of both the adopted action recognition approach and the proposed minimum class variance extreme learning machine algorithm.
---
paper_title: View and Style-Independent Action Manifolds for Human Activity Recognition

paper_content:

We introduce a novel approach to automatically learn intuitive and compact descriptors of human body motions for activity recognition. Each action descriptor is produced, first, by applying Temporal Laplacian Eigenmaps to view-dependent videos in order to produce a stylistic invariant embedded manifold for each view separately. Then, all view-dependent manifolds are automatically combined to discover a unified representation which model in a single three dimensional space an action independently from style and viewpoint. In addition, a bidirectional nonlinear mapping function is incorporated to allow projecting actions between original and embedded spaces. The proposed framework is evaluated on a real and challenging dataset (IXMAS), which is composed of a variety of actions seen from arbitrary viewpoints. Experimental results demonstrate robustness against style and view variation and match the most accurate action recognition method.
---
paper_title: Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis

paper_content:

In this paper, we present a view-independent action recognition method exploiting a low computational-cost volumetric action representation. Binary images depicting the human body during action execution are accumulated in order to produce the so-called action volumes. A novel time-invariant action representation is obtained by exploiting the circular shift invariance property of the magnitudes of the Discrete Fourier Transform coefficients. The similarity of an action volume with representative action volumes is exploited in order to map it to a lower-dimensional feature space that preserves the action class properties. Discriminant learning is, subsequently, employed for further dimensionality reduction and action class discrimination. By using such an action representation, the proposed approach performs fast action recognition. By combining action recognition results coming from different view angles, high recognition rates are obtained. The proposed method is extended to interaction recognition, i.e., to human action recognition involving two persons. The proposed approach is evaluated on a publicly available action recognition database using experimental settings simulating situations that may appear in real-life applications, as well as on a new nutrition support action recognition database.
---
paper_title: Activity-Based Person Identification Using Fuzzy Representation and Discriminant Learning

paper_content:

In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which “walk” (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy vector quantization and linear discriminant analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition, and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.
---
paper_title: Human action recognition using multiple views: a comparative perspective on recent developments

paper_content:

This paper presents a review and comparative study of recent multi-view 2D and 3D approaches for human action recognition. The approaches are reviewed and categorized due to their nature. We report a comparison of the most promising methods using two publicly available datasets: the INRIA Xmas Motion Acquisition Sequences (IXMAS) and the i3DPost Multi-View Human Action and Interaction Dataset. Additionally, we discuss some of the shortcomings of multi-view camera setups and outline our thoughts on future directions of 3D human action recognition.
---
paper_title: Fuzzy action recognition for multiple views within single camera

paper_content:

To be able to perform human action recognition from multiple views is a great challenge in the field of computer vision. State-of-the-art solutions have been focusing on building a 3D action model from multiple views in a multi, calibrated cameras' environment. Promising results were achieved; however, these approaches tend to assume that human action is performed frontal-parallel to each of the multiple cameras. In a real world scenario, this is not always true and the overlapping regions in such systems are very limited. In this paper, we proposed a fuzzy action recognition framework for multiple views within a single camera. We adopted fuzzy quantity space in the framework and introduced a new concept called the Signature Action Behaviour to model an action from multiple views and represent it as fuzzy descriptor. Then, distance measure is applied to deduce an action. Experimental results showed the efficiency of our proposed framework in modeling the actions from different viewpoints and styles.
---
paper_title: View and Style-Independent Action Manifolds for Human Activity Recognition

paper_content:

We introduce a novel approach to automatically learn intuitive and compact descriptors of human body motions for activity recognition. Each action descriptor is produced, first, by applying Temporal Laplacian Eigenmaps to view-dependent videos in order to produce a stylistic invariant embedded manifold for each view separately. Then, all view-dependent manifolds are automatically combined to discover a unified representation which model in a single three dimensional space an action independently from style and viewpoint. In addition, a bidirectional nonlinear mapping function is incorporated to allow projecting actions between original and embedded spaces. The proposed framework is evaluated on a real and challenging dataset (IXMAS), which is composed of a variety of actions seen from arbitrary viewpoints. Experimental results demonstrate robustness against style and view variation and match the most accurate action recognition method.
---
paper_title: Linguistic summarization of video for fall detection using voxel person and fuzzy logic

paper_content:

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person's states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.
---
paper_title: Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes

paper_content:

A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique utilization of particle trajectories for modeling crowded scenes, in which we propose new and efficient representative trajectories for modeling arbitrarily complicated crowd flows. Second, chaotic dynamics are introduced into the crowd context to characterize complicated crowd motions by regulating a set of chaotic invariant features, which are reliably computed and used for detecting anomalies. Third, a probabilistic framework for anomaly detection and localization is formulated. The overall work-flow begins with particle advection based on optical flow. Then particle trajectories are clustered to obtain representative trajectories for a crowd flow. Next, the chaotic dynamics of all representative trajectories are extracted and quantified using chaotic invariants known as maximal Lyapunov exponent and correlation dimension. Probabilistic model is learned from these chaotic feature set, and finally, a maximum likelihood estimation criterion is adopted to identify a query video of a scene as normal or abnormal. Furthermore, an effective anomaly localization algorithm is designed to locate the position and size of an anomaly. Experiments are conducted on known crowd data set, and results show that our method achieves higher accuracy in anomaly detection and can effectively localize anomalies.
---
paper_title: Extension of a soft-computing framework for activity analysis from linguistic summarizations of video

paper_content:

Video cameras are a relatively low-cost, rich source of information that can be used for ldquowell-beingrdquo assessment and abnormal event detection for the goal of allowing elders to live longer and healthier independent lives. We previously reported a soft-computing fall detection system, based on two levels from a hierarchy of fuzzy inference using linguistic summarizations of activity acquired temporally from a three dimensional voxel representation of the human found by back projecting silhouettes acquired from multiple cameras. This framework is extremely flexible and rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. In this paper, we show the flexibility of our activity analysis framework by extending it to additional common elderly activities and contextual awareness is added for reasoning based on location or static objects in the apartment.
---
paper_title: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models

paper_content:

Extremely crowded scenes present unique challenges to video analysis that cannot be addressed with conventional approaches. We present a novel statistical framework for modeling the local spatio-temporal motion pattern behavior of extremely crowded scenes. Our key insight is to exploit the dense activity of the crowded scene by modeling the rich motion patterns in local areas, effectively capturing the underlying intrinsic structure they form in the video. In other words, we model the motion variation of local space-time volumes and their spatial-temporal statistical behaviors to characterize the overall behavior of the scene. We demonstrate that by capturing the steady-state motion behavior with these spatio-temporal motion pattern models, we can naturally detect unusual activity as statistical deviations. Our experiments show that local spatio-temporal motion pattern modeling offers promising results in real-world scenes with complex activities that are hard for even human observers to analyze.
---
paper_title: Modeling Human Activity From Voxel Person Using Fuzzy Logic

paper_content:

As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.
---
paper_title: Fall detection in a smart room by using a fuzzy one class support vector machine and imperfect training data

paper_content:

In this paper, we propose an efficient and robust fall detection system by using a fuzzy one class support vector machine based on video information. Two cameras are used to capture the video frames from which the features are extracted. A fuzzy one class support vector machine (FOCSVM) is used to distinguish falling from other activities, such as walking, sitting, standing, bending or lying. Compared with the traditional one class support vector machine, the FOCSVM can obtain a more accurate and tight decision boundary under a training dataset with outliers. From real video sequences, the success of the method is confirmed with less non-fall samples being misclassified as falls by the classifier under an imperfect training dataset.
---
paper_title: Computing with uncertainty

paper_content:

The role of and interaction between statistical, fuzzy, and neural-like models for certain problems associated with the three main areas of pattern recognition system design are discussed. Some questions concerning fuzzy sets are answered, and the design of fuzzy pattern recognition systems is reviewed. Pattern recognition, statistical pattern recognition and fuzzy pattern recognition systems are described. The use of computational neural-like networks in fuzzy pattern recognition is also discussed. >
---
paper_title: Uncertainty representation using fuzzy measures

paper_content:

We introduce the fuzzy measure and discuss its use as a unifying structure for modeling knowledge about an uncertain variable. We show that a large class of well-established types of uncertainty representations can be modeled within this framework. A view of the Dempster-Shafer (D-S) belief structure as an uncertainty representation corresponding to a set of possible fuzzy measures is discussed. A methodology for generating this set of fuzzy measures from a belief structure is described. A measure of entropy associated with a fuzzy measure is introduced and its manifestation for different fuzzy measures is described. The problem of uncertain decision making for the case in which the uncertainty represented by a fuzzy measure is considered. The Choquet integral is introduced as providing a generalization of the expected value to this environment.
---
paper_title: Representation of Uncertainty in Computer Vision Using Fuzzy Sets

paper_content:

Uncertainty in computer vision can arise at various levels. It can occur in the low level in the raw sensor input, and extends all the way through intermediate and higher levels. Ideally, at any level where decisions are being made on the basis of previous processing steps, a computer vision system must have sufficient flexibility for representation of uncertainty in any of these levels.
---
paper_title: Cluster labeling and parameter estimation for the automated setup of a hand-gesture recognition system

paper_content:

In this work, we address the issue of reconfigurability of a hand-gesture recognition system. The calibration or setup of the operational parameters of such a system is a time-consuming effort, usually performed by trial and error, and often causing system performance to suffer because of designer impatience. In this work, we suggest a methodology using a neighborhood-search algorithm for tuning system parameters. Thus, the design of hand-gesture recognition systems is transformed into an optimization problem. To test the methodology, we address the difficult problem of simultaneous calibration of the parameters of the image processing/fuzzy C-means (FCM) components of a hand-gesture recognition system. In addition, we proffer a method for supervising the FCM algorithm using linear programming and heuristic labeling. Resulting solutions exhibited fast convergence (in the order of ten iterations) to reach recognition accuracies within several percent of the optimal. Comparative performance testing using three gesture databases (BGU, American Sign Language and Gripsee), and a real-time implementation (Tele-Gest) are reported on.
---
paper_title: Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems

paper_content:

We derive inner- and outer-bound sets for the type-reduced set of an interval type-2 fuzzy logic system (FLS), based on a new mathematical interpretation of the Karnik-Mendel iterative procedure for computing the type-reduced set. The bound sets can not only provide estimates about the uncertainty contained in the output of an interval type-2 FLS, but can also be used to design an interval type-2 FLS. We demonstrate, by means of a simulation experiment, that the resulting system can operate without type-reduction and can achieve similar performance to one that uses type-reduction. Therefore, our new design method, based on the bound sets, can relieve the computation burden of an interval type-2 FLS during its operation, which makes an interval type-2 FLS useful for real-time applications.
---
paper_title: Vision based hand gesture recognition using finite state machines and fuzzy logic

paper_content:

This paper proposes a finite state and fuzzy logic based approach to hand gesture learning and recognition. The location of 2D image positions of the hands of the user is obtained by Edge detection and vector extraction. These are used to identify the hand posture as well as the center of the hand of the user. We first learn the spatial information without data segmentation and alignment. Then the data is grouped into clusters that are associated with information for temporal alignment. The points thus obtained are clustered using Fuzzy c-mean clustering algorithm. These clusters of hand posture further determine the states of the Finite State Machine(s) through which the succeeding gesture has to be matched. To build a Gesture Recognizer (GR) the temporal information is integrated. Each hand gesture is defined to be an ordered sequence of states in spatial-temporal space i.e. FSM corresponding to it. The number of states/clusters in an FSM represents a trade-off between the accuracy of gesture recognizer and the amount of spatial-temporal data it stores.
---
paper_title: Real-time hand gesture telerobotic system using fuzzy c-means clustering

paper_content:

This paper describes a teleoperation system in which an articulated robot performs a block pushing task based on hand gesture commands sent through the Internet. A fuzzy c-means clustering method is used to classify hand postures as "gesture commands". The fuzzy recognition system was tested using 20 trials each of a 12-gesture vocabulary. Results revealed an acceptance rate of 99.6% (percent of gestures with a sufficiently large membership value to belong to at least one of the designated classifications), and a recognition accuracy of 100% (the percent of accepted gestures classified correctly). Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.
---
paper_title: Uncertainty measures for interval type-2 fuzzy sets

paper_content:

Fuzziness (entropy) is a commonly used measure of uncertainty for type-1 fuzzy sets. For interval type-2 fuzzy sets (IT2 FSs), centroid, cardinality, fuzziness, variance and skewness are all measures of uncertainties. The centroid of an IT2 FS has been defined by Karnik and Mendel. In this paper, the other four concepts are defined. All definitions use a Representation Theorem for IT2 FSs. Formulas for computing the cardinality, fuzziness, variance and skewness of an IT2 FS are derived. These definitions should be useful in IT2 fuzzy logic systems design using the principles of uncertainty, and in measuring the similarity between two IT2 FSs.
---
paper_title: Type-2 Fuzzy Sets for Handling Uncertainty in Pattern Recognition

paper_content:

This paper presents type-2 fuzzy sets (T2 FSs) to handle both fuzzy and random uncertainties in pattern recognition problems. We integrate T2 FSs with Markov processes (MPs) for speech recognition and handwritten Chinese character recognition. Experimental results show that T2 FSs can improve the classification performance in practical applications.
---
paper_title: Fuzzy qualitative complex actions recognition

paper_content:

Understanding actions is a complex issue in many aspects. However, most of the literature on action recognition deals with only simple actions. In this paper, we proposed the fuzzy qualitative robot kinematics framework to complex actions over time, e.g. walk then run and over the body, walk while wave hand etc. The human limbs is modelled as articulated rigid bodies and its motion is represented by a series of such models in terms of time. With this, we eventually converted the human motion analysis into a conventional robotic problem which has been well studied. Experimental results has shown that the action model built in this manifold offers few advantages. e.g. handles the tradeoffs in the off-the-shelf tracking algorithm and avoid using generative model where the size of the training data typically goes as the square of the number of states.
---
paper_title: A fuzzy qualitative approach to human motion recognition

paper_content:

The understanding of human motions captured in image sequences pose two main difficulties which are often regarded as computationally ill-defined: 1) modelling the uncertainty in the training data, and 2) constructing a generic activity representation that can describe simple actions as well as complicated tasks that are performed by different humans. In this paper, these problems are addressed from a direction which utilises the concept of fuzzy qualitative robot kinematics. First of all, the training data representing a typical activity is acquired by tracking the human anatomical landmarks in an image sequences. Then, the uncertainty arise when the limitations of the tracking algorithm are handled by transforming the continuous training data into a set of discrete symbolic representations - qualitative states in a quantisation process. Finally, in order to construct a template that is regarded as a combination ordered sequence of all body segments movements, robot kinematics, a well-defined solution to describe the resulting motion of rigid bodies that form the robot, has been employed. We defined these activity templates as qualitative normalised templates, a manifold trajectory of unique state transition patterns in the quantity space. Experimental results and a comparison with the hidden Markov models have demonstrated that the proposed method is very encouraging and shown a better successful recognition rate on the two available motion databases.
---
paper_title: Fuzzy Qualitative Human Motion Analysis

paper_content:

This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.
---
paper_title: Person specific activity recognition using fuzzy learning and Discriminant Analysis

paper_content:

One of the major issues that activity recognition methods should be able to face is the style variations observed in the execution of activities performed by different humans. In order to address this issue we propose a person-specific activity recognition framework in which human identification proceeds activity recognition. After recognizing the ID of the human depicted in a video stream, a person-specific activity classifier is responsible to recognize the activity performed by the human. Exploiting the enriched human body information captured by a multi-camera setup, view-invariant person and activity representations are obtained. The classification procedure involves Fuzzy Vector Quantization and Linear Discriminant Analysis. The proposed method is applied on drinking and eating activity recognition as well as on other activity recognition tasks. Experiments show that the person-specific approach outperforms the person-independent one.
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paper_title: Activity-Based Person Identification Using Fuzzy Representation and Discriminant Learning

paper_content:

In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which “walk” (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy vector quantization and linear discriminant analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition, and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.
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paper_title: Linguistic summarization of video for fall detection using voxel person and fuzzy logic

paper_content:

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person's states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.
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paper_title: Linguistic description of adult skeletal age-at-death estimations from fuzzy integral acquired fuzzy sets

paper_content:

Previously, we introduced a novel method to estimate adult skeletal age-at-death using the Sugeno fuzzy integral (FI). Specifically, we took a multi-hypothesis testing approach to make the classical FI yield a fuzzy set (FS)-valued result, which is not guaranteed to be normal or convex, based on interval-valued sources of information (aging methods). We showed quantitative results for summarizing the FS and comparing the single decision to a known age-at-death. In this article, we extend our prior work and present formulas to measure the uncertainty in the resultant FSs. We generate linguistic descriptions to establish domain standardization for the goal of assisting forensic and biological anthropologists. Specifically, we introduce fuzzy class definitions for age-at-death FSs and we present an OWA contrast approach to measure the degree of specificity in age-at-death FSs.
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paper_title: A Fuzzy Measure Similarity Between Sets of Linguistic Summaries

paper_content:

In this paper, we consider the problem of evaluating the similarity of two sets of linguistic summaries of sensor data. Huge amounts of available data cause a dramatic need for summarization. In continuous monitoring, it is useful to compare one time interval of data with another, for example, to detect anomalies or to predict the onset of a change from a normal state. Assuming that summaries capture the essence of the data, it is sufficient to compare only those summaries, i.e., they are descriptive features for recognition. In previous work, we developed a similarity measure between two individual summaries and proved that the associated dissimilarity is a metric. Additionally, we proposed some basic methods to combine these similarities into an aggregate value. Here, we develop a novel parameter free method, which is based on fuzzy measures and integrals, to fuse individual similarities that will produce a closeness measurement between sets of summaries. We provide a case study from the eldercare domain where the goal is to compare different nighttime patterns for change detection. The reasons for studying linguistic summaries for eldercare are twofold: First, linguistic summaries are the natural communication tool for health care providers in a decision support system, and second, due to the extremely large volume of raw data, these summaries create compact features for an automated reasoning for detection and prediction of health changes as part of the decision support system.
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paper_title: Linguistic summarization of the human activity using skin conductivity and accelerometers

paper_content:

This paper addresses the problem of human activity monitoring including data about the degree of accumulated mental stress. We describe a simple experiment aimed at exploring the possibilities of providing people with linguistic feedback of the temporal evolution of their stress and concentration capability during the working day. We provide details about the utilized sensors and describe how to use fuzzy logic and a finite states machine to create a linguistic model of the evolution of these parameters with the time. Some experimental results are included.
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paper_title: LINGUISTIC SUMMARIES OF DATA USING FUZZY LOGIC

paper_content:

We present basic ideas and perspectives related to the use of fuzzy logic for the derivation of linguistic summaries of data sets (databases in practice). We concentrate on the issue of how to measure the goodness of a linguistic summary. We advocate the use of an interactive approach in which the user indicates first the class of linguistic summaries of interest (basically, by specifying attributes relation between which he or she is interested in) by using a fuzzy querying interface to a database. Finally, we present an implementation for deriving linguistic summaries of a sales database at a computer retailer, and show how the linguistic summaries obtained can be useful for supporting decisions by the business owner.
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paper_title: Computing with words

paper_content:

Computing with words is defined to be an extension of fuzzy logic, which permits self-referential generalization. It entails the randomization of declarative knowledge, which yields procedural knowledge. Such randomization can occur at two levels. The first is termed weak randomization, which is essentially a domain-general pattern matching operation. The second is termed strong randomization, which entails the application of one rule set to the semantics of another possibly including itself. Strong randomization rests on top of weak randomization. Strong randomization is essentially a heuristic process. It is fully scalable, since it can in theory map out its own needed heuristics for ever-more efficient search, including segmentation of the knowledge base. It is proven that strong learning must be knowledge-based, if effective. Computing with words does not preclude the use of predicate functions and procedural attachments. It may well be that the presented algorithm serves to unify programming and computing with words to yield an artificial intelligence.
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paper_title: Fuzzy logic = computing with words

paper_content:

As its name suggests, computing with words (CW) is a methodology in which words are used in place of numbers for computing and reasoning. The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa. Thus, as an approximation, fuzzy logic may be equated to CW. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW. In CW, a word is viewed as a label of a granule; that is, a fuzzy set of points drawn together by similarity, with the fuzzy set playing the role of a fuzzy constraint on a variable. The premises are assumed to be expressed as propositions in a natural language. In coming years, computing with words is likely to evolve into a basic methodology in its own right with wide-ranging ramifications on both basic and applied levels.
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paper_title: Linguistic summarization of sensor data for eldercare

paper_content:

Ubiquitous passive, as well as active, monitoring of elders is a growing field of research and development with the goal of allowing seniors to live safe active independent lives with minimal intrusion. Much useful information, fall detection, fall risk assessment, activity recognition, early illness detection, etc. can be inferred from the mountain of data. Healthcare must be human centric and human friendly, and so, methods to consolidate the data into linguistic summaries for enhanced communication and problem detection with elders, family and healthcare providers is essential. Long term trends can be most easily identified using summarized information. This paper explores the soft computing methodology of protoforms to produce linguistic summaries of one dimensional data, motion and restlessness. The technique is demonstrated on a 15 month sensor collection for an elder participant.
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paper_title: Neuro – Fuzzy Rule Generation : Survey in Soft Computing Framework

paper_content:

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.
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paper_title: A new approach to fuzzy rule generation: fuzzy extension matrix

paper_content:

This paper proposes a new approach to fuzzy rule generation from a set of examples with fuzzy representation. The new approach called fuzzy extension matrix incorporates the fuzzy entropy to search for paths and generalizes the concept of crisp extension matrix. By discussing paths of the fuzzy extension matrix, a new heuristic algorithm for generating fuzzy rules is introduced. Compared with the crisp extension matrix, the proposed method has the capability of handling fuzzy representation and tolerating noisy data or missing data. A case study shows that the proposed heuristic algorithm partially inherits the advantages from the crisp case such as simplicity of rules and high learning accuracy. The proposed approach offers a new, practical way to automatically acquire imprecise knowledge.
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paper_title: Fuzzy rule generation methods for high-level computer vision

paper_content:

Abstract In many decision making systems involving multiple sources, the decisions made may be considered as the result of a rule-based system in which the decision rules are usually enumerated by experts or generated by a learning process. In this paper, we discuss the various issues involved in the generation of fuzzy rules automatically from training data for high-level computer vision. Features are treated as linguistic variables that appear in the antecedent clauses of the rules. We present methods to generate the corresponding linguistic labels (values) and their membership functions. Rules are generated by constructing a minimal approximate fuzzy aggregation network and then training the network using gradient descent methods. Several examples are given.
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paper_title: Fast clustering with application to fuzzy rule generation

paper_content:

This paper presents a multistage random sampling fuzzy c-means based clustering algorithm, which significantly reduces the computation time required to partition a data set into c classes. A series of subsets of the full data set are used for classification in order to provide an approximation to the final cluster centers. The quality of the final partitions is equivalent to that of fuzzy c-means. The speed-up is normally a factor of 2-3 times, which is especially significant for high dimensional spaces and large data sets. Clustering has been used to generate fuzzy rules for control. In this paper, we show that the multistage random sampling fuzzy c-means based clustering algorithm can be effectively used to create fuzzy rules in the domain of magnetic resonance images where over 60,000 patterns and 3 features or attributes are common. >
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paper_title: Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base

paper_content:

A method is proposed to automatically learn the knowledge base by finding an appropiate data base by means of a genetic algorithm while using a simple generation method to derive the rule base. Our genetic process learns the number of linguistic terms per variable and the membership function parameters that define their semantics, while a rule base generation method learns the number of rules and their composition.
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paper_title: Generating fuzzy rules by learning from examples

paper_content:

A general method is developed for generating fuzzy rules from numerical data. The method consists of five steps: dividing the input and output spaces of the given numerical data into fuzzy regions; generating fuzzy rules from the given data; assigning a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; creating a combined fuzzy-associative-memory (FAM) bank based on both the generated rules and linguistic rules of human experts; and determining a mapping from input space to output space based on the combined FAM bank using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. The method is applied to predicting a chaotic time series. >
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paper_title: A fuzzy qualitative approach for scene classification

paper_content:

Scene classification has been studied extensively in the recent past. Most of the state-of-the-art solutions assumed that scene classes are mutually exclusive. However, this is not true as a scene image may belongs to multiple classes and different people are tend to respond inconsistently even given a same scene image. In this paper, we propose a fuzzy qualitative approach to address this problem. That is, we first adopted the fuzzy quantity space to model the training data. Secondly, we present a novel weight function, w to train a fuzzy qualitative scene model in the fuzzy qualitative states. Finally, we introduce fuzzy qualitative partition to perform the scene classification. Empirical results using a standard dataset and a comparison with K-nearest neighbour has shown the effectiveness and robustness of the proposed method.
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paper_title: Learning multi-label scene classification

paper_content:

In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in semantic scene and document classification and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a field scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a different treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classification; furthermore, our work appears to generalize to other classification problems of the same nature.
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paper_title: Relative attributes

paper_content:

Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is ‘smiling’ or not, a scene is ‘dry’ or not), and thus fail to capture more general semantic relationships. We propose to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over the joint space of attribute ranking outputs, and propose a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, ‘bears are furrier than giraffes’). We further show how the proposed relative attributes enable richer textual descriptions for new images, which in practice are more precise for human interpretation. We demonstrate the approach on datasets of faces and natural scenes, and show its clear advantages over traditional binary attribute prediction for these new tasks.
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paper_title: Recognizing human actions from still images with latent poses

paper_content:

We consider the problem of recognizing human actions from still images. We propose a novel approach that treats the pose of the person in the image as latent variables that will help with recognition. Different from other work that learns separate systems for pose estimation and action recognition, then combines them in an ad-hoc fashion, our system is trained in an integrated fashion that jointly considers poses and actions. Our learning objective is designed to directly exploit the pose information for action recognition. Our experimental results demonstrate that by inferring the latent poses, we can improve the final action recognition results.
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paper_title: Action Recognition with Improved Trajectories

paper_content:

Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.
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paper_title: Weakly Supervised Learning of Interactions between Humans and Objects

paper_content:

We introduce a weakly supervised approach for learning human actions modeled as interactions between humans and objects. Our approach is human-centric: We first localize a human in the image and then determine the object relevant for the action and its spatial relation with the human. The model is learned automatically from a set of still images annotated only with the action label. Our approach relies on a human detector to initialize the model learning. For robustness to various degrees of visibility, we build a detector that learns to combine a set of existing part detectors. Starting from humans detected in a set of images depicting the action, our approach determines the action object and its spatial relation to the human. Its final output is a probabilistic model of the human-object interaction, i.e., the spatial relation between the human and the object. We present an extensive experimental evaluation on the sports action data set from [1], the PASCAL Action 2010 data set [2], and a new human-object interaction data set.
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paper_title: Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition

paper_content:

Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene or event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape or appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.
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paper_title: Discriminative models for static human-object interactions

paper_content:

We advocate an approach to activity recognition based on modeling contextual interactions between postured human bodies and nearby objects. We focus on the difficult task of recognizing actions from static images and formulate the problem as a latent structured labeling problem. We develop a unified, discriminative model for such context-based action recognition building on recent techniques for learning large-scale discriminative models. The resulting contextual models learned by our system outperform previously published results on a database of sports actions.
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paper_title: Max-margin early event detectors

paper_content:

The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling early detection. Our method is based on Structured Output SVM, but extends it to accommodate sequential data. Experiments on datasets of varying complexity, for detecting facial expressions, hand gestures, and human activities, demonstrate the benefits of our approach. To the best of our knowledge, this is the first paper in the literature of computer vision that proposes a learning formulation for early event detection.
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paper_title: Action recognition from a distributed representation of pose and appearance

paper_content:

We present a distributed representation of pose and appearance of people called the “poselet activation vector”. First we show that this representation can be used to estimate the pose of people defined by the 3D orientations of the head and torso in the challenging PASCAL VOC 2010 person detection dataset. Our method is robust to clutter, aspect and viewpoint variation and works even when body parts like faces and limbs are occluded or hard to localize. We combine this representation with other sources of information like interaction with objects and other people in the image and use it for action recognition. We report competitive results on the PASCAL VOC 2010 static image action classification challenge.
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paper_title: Recognizing human actions in still images: a study of bag-of-features and part-based representations

paper_content:

Recognition of human actions is usually addressed in the scope of video interpretation. Meanwhile, common human actions such as ''reading a book'', ''playing a guitar'' or ''writing notes'' also provide a natural description for many still images. In addition, some actions in video such as ''taking a photograph'' are static by their nature and may require recognition methods based on static cues only. Motivated by the potential impact of recognizing actions in still images and the little attention this problem has received in computer vision so far, we address recognition of human actions in consumer photographs. We construct a new dataset available at http://www.di.ens.fr/willow/research/stillactions/ with seven classes of actions in 911 Flickr images representing natural variations of human actions in terms of camera view-point, human pose, clothing, occlusions and scene background. We study action recognition in still images using the state-of-the-art bag-of-features methods as well as their combination with the part-based Latent SVM approach of Felzenszwalb et al. In particular, we investigate the role of background scene context and demonstrate that improved action recognition performance can be achieved by (i) combining the statistical and part-based representations, and (ii) integrating person-centric description with the background scene context. We show results on our newly collected dataset of seven common actions as well as demonstrate improved performance over existing methods on the datasets of Gupta et al. and Yao and Fei-Fei.
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paper_title: Human activity prediction: Early recognition of ongoing activities from streaming videos

paper_content:

In this paper, we present a novel approach of human activity prediction. Human activity prediction is a probabilistic process of inferring ongoing activities from videos only containing onsets (i.e. the beginning part) of the activities. The goal is to enable early recognition of unfinished activities as opposed to the after-the-fact classification of completed activities. Activity prediction methodologies are particularly necessary for surveillance systems which are required to prevent crimes and dangerous activities from occurring. We probabilistically formulate the activity prediction problem, and introduce new methodologies designed for the prediction. We represent an activity as an integral histogram of spatio-temporal features, efficiently modeling how feature distributions change over time. The new recognition methodology named dynamic bag-of-words is developed, which considers sequential nature of human activities while maintaining advantages of the bag-of-words to handle noisy observations. Our experiments confirm that our approach reliably recognizes ongoing activities from streaming videos with a high accuracy.
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paper_title: Grouplet: A structured image representation for recognizing human and object interactions

paper_content:

Psychologists have proposed that many human-object interaction activities form unique classes of scenes. Recognizing these scenes is important for many social functions. To enable a computer to do this is however a challenging task. Take people-playing-musical-instrument (PPMI) as an example; to distinguish a person playing violin from a person just holding a violin requires subtle distinction of characteristic image features and feature arrangements that differentiate these two scenes. Most of the existing image representation methods are either too coarse (e.g. BoW) or too sparse (e.g. constellation models) for performing this task. In this paper, we propose a new image feature representation called “grouplet”. The grouplet captures the structured information of an image by encoding a number of discriminative visual features and their spatial configurations. Using a dataset of 7 different PPMI activities, we show that grouplets are more effective in classifying and detecting human-object interactions than other state-of-the-art methods. In particular, our method can make a robust distinction between humans playing the instruments and humans co-occurring with the instruments without playing.
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paper_title: Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses

paper_content:

Detecting objects in cluttered scenes and estimating articulated human body parts from 2D images are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g., playing tennis), where the relevant objects tend to be small or only partially visible and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other-recognizing one facilitates the recognition of the other. In this paper, we propose a mutual context model to jointly model objects and human poses in human-object interaction activities. In our approach, object detection provides a strong prior for better human pose estimation, while human pose estimation improves the accuracy of detecting the objects that interact with the human. On a six-class sports data set and a 24-class people interacting with musical instruments data set, we show that our mutual context model outperforms state of the art in detecting very difficult objects and estimating human poses, as well as classifying human-object interaction activities.
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paper_title: Activity forecasting

paper_content:

We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our unified model also integrates several other key elements of activity analysis, namely, destination forecasting, sequence smoothing and transfer learning. As proof-of-concept, we focus on the domain of trajectory-based activity analysis from visual input. Experimental results demonstrate that our model accurately predicts distributions over future actions of individuals. We show how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.
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Title: Fuzzy Human Motion Analysis: A Review

Section 1: Introduction
Description 1: Provide an overview of Human Motion Analysis (HMA) along with its applications and the evolution of methodologies over the years. Highlight the current advancements and present the motivation behind focusing on fuzzy approaches in HMA.

Section 2: Motivation and Contributions
Description 2: Discuss the reasons for employing fuzzy set theory in HMA, outline the unique contributions of the paper, and highlight how this survey contrasts with previous works.

Section 3: Low-Level HMA
Description 3: Discuss methods in detecting and segmenting humans from images using fuzzy approaches, including motion segmentation and object classification.

Section 4: Motion Segmentation
Description 4: Review fuzzy approaches used in background subtraction and motion segmentation, emphasizing the fuzzy integral, type-2 Gaussian mixture model, and hybrid techniques.

Section 5: Object Classification
Description 5: Explore fuzzy approaches in object classification to distinguish between human and non-human objects, covering Type-1 Fuzzy Inference System (FIS) and Type-2 FIS.

Section 6: Mid-Level HMA
Description 6: Detail methods for human tracking using both model-based and non-model-based approaches, including fuzzy qualitative kinematics, voxel person, shape estimation, fuzzy Kalman filter, particle filter, optical flow, and clustering techniques.

Section 7: High-Level HMA
Description 7: Describe higher-level tasks in human motion analysis such as hand gesture recognition, activity recognition with fuzzy vector quantization, qualitative normalized template, fuzzy hidden Markov model, style invariant action recognition, and multi-view action recognition.

Section 8: Gesture Recognition
Description 8: Discuss fuzzy clustering algorithms and the integration of fuzzy approaches with machine learning methods for recognizing hand gestures under complex conditions.

Section 9: Activity Recognition
Description 9: Review techniques for recognizing ongoing activities, including FIS, fuzzy HMM, FVQ, QNT, and handling uncertainty in feature data.

Section 10: Style Invariant Action Recognition
Description 10: Discuss approaches to recognize actions performed by different people in different styles using person-specific fuzzy motion models.

Section 11: Multi-View Action Recognition
Description 11: Explore methods to achieve view invariant human action recognition, including multi-view fuzzy motion models and single-camera frameworks.

Section 12: Anomaly Event Detection
Description 12: Review techniques like fuzzy one class support vector machine for detecting anomalies in human behavior.

Section 13: Discussion
Description 13: Discuss important factors contributing to the success of fuzzy approaches in HMA, and potential directions for future research.

Section 14: Conclusion
Description 14: Summarize the key findings regarding the effectiveness of fuzzy set theory in HMA and suggest future research opportunities.

Section 15: Potential Future Works in Fuzzy HMA
Description 15: Identify future research directions, such as creating fuzzy datasets, employing public datasets as baselines, early event detection, and human activity recognition in still images.