A Survey Of Activity Recognition And Understanding The Behavior In Video Survelliance
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paper_title: Statistical Background Modeling: An Edge Segment Based Moving Object Detection Approach

paper_content:

We propose an edge segment based statistical background modeling algorithm and a moving edge detection framework for the detection of moving objects. We analyze the performance of the proposed segment based statistical background model with traditional pixel based, edge pixel based and edge segment based approaches. Existing edge based moving object detection algorithms fetches difficulty due to the change in background motion, object shape, illumination variation and noise. The proposed algorithm makes efficient use of statistical background model using the edge-segment structure. Experiments with natural image sequences show that our method can detect moving objects efficiently under the above mentioned environments.
---
paper_title: Adaptive mean-shift for automated multi object tracking

paper_content:

Mean-shift tracking plays an important role in computer vision applications because of its robustness, ease of implementation and computational efficiency. In this study, a fully automatic multiple-object tracker based on mean-shift algorithm is presented. Foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the object trackers and also used as a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to converge for the new location of the object. By using foreground detection, new objects entering to the field of view and objects that are leaving the scene could be detected. Trackers are automatically refreshed to solve the potential problems that may occur because of the changes in objects' size, shape, to handle occlusion-split between the tracked objects and to detect newly emerging objects as well as objects that leave the scene. Using a shadow removal method increases the tracking accuracy. As a result, a method that remedies problems of mean-shift tracking and presents an easy to implement, robust and efficient tracking method that can be used for automated static camera video surveillance applications is proposed. Additionally, it is shown that the proposed method is superior to the standard mean-shift.
---
paper_title: Bayesian visual surveillance: A model for detecting and tracking a variable number of moving objects

paper_content:

An automatic detection and tracking framework for visual surveillance is proposed, which is able to handle a variable number of moving objects. Video object detectors generate an unordered set of noisy, false, missing, split, and merged measurements that make extremely complex the tracking task. Especially challenging are split detections (one object is split into several measurements) and merged detections (several objects are merged into one detection). Few approaches address this problem directly, and the existing ones use heuristics methods, or assume a known number of objects, or are not suitable for on-line applications. In this paper, a Bayesian Visual Surveillance Model is proposed that is able to manage undesirable measurements. Particularly, split and merged measurements are explicitly modeled by stochastic processes. Inference is accurately performed through a particle filtering approach that combines ancestral and MCMC sampling. Experimental results have shown a high performance of the proposed approach in real situations.
---
paper_title: Multi-Feature Fusion Based Object Detecting and Tracking

paper_content:

A new approach is proposed to detect and track the moving object. The affine motion model and the non-parameter distribution model are utilized to represent the object firstly. Then the motion region of the object is detected by background difference while Kalman filter estimating its affine motion in next frame. Center association and mean shift are adopted to obtain the observation values. Finally, the distance variance and scale variance between the estimated and detected regions are used to fuse the observation values to acquire the measurement value. To correct fusion errors, the observable edges are employed. Experimental results show that the new method can successfully track the object under such case as merging, splitting, scale variation and scene noise.
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paper_title: Keynote talk 4: Automated understanding of video object events in a distributed smart camera network

paper_content:

Summary form only given. You may think that your requirements engineering process is fairly limited and currently not too complex to manage. Or you may be right in the middle of a storm of requirements where it is impossible to manage even the most important ones comprehensively. If you are in the former situation, it may be dangerous to relax as many successful software-intensive products have a tendency to grow very rapidly in size and complexity, and sooner than you might think you may be hit by a massive flood of feature requests and too few resources to prevent the flood from drowning your development organization, which in turn put your future innovation capability and competitiveness at risk.
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paper_title: Real-time Object Classification in Video Surveillance Based on Appearance Learning

paper_content:

Classifying moving objects to semantically meaningful categories is important for automatic visual surveillance. However, this is a challenging problem due to the factors related to the limited object size, large intra-class variations of objects in a same class owing to different viewing angles and lighting, and real-time performance requirement in real-world applications. This paper describes an appearance-based method to achieve real-time and robust objects classification in diverse camera viewing angles. A new descriptor, i.e., the multi-block local binary pattern (MB-LBP), is proposed to capture the large-scale structures in object appearances. Based on MB-LBP features, an adaBoost algorithm is introduced to select a subset of discriminative features as well as construct the strong two-class classifier. To deal with the non-metric feature value of MB-LBP features, a multi-branch regression tree is developed as the weak classifiers of the boosting. Finally, the error correcting output code (ECOC) is introduced to achieve robust multi-class classification performance. Experimental results show that our approach can achieve real-time and robust object classification in diverse scenes.
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paper_title: ViBe: A Universal Background Subtraction Algorithm for Video Sequences

paper_content:

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.
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paper_title: Automatic Reasoning about Causal Events in Surveillance Video

paper_content:

We present a new method for explaining causal interactions among people in video. The input to the overall system is video in which people are low/medium resolution. We extract and maintain a set of qualitative descriptions of single-person activity using the low-level vision techniques of spatiotemporal action recognition and gaze-direction approximation. This models the input to the "sensors" of the person agent in the scene and is a general sensing strategy for a person agent in a variety of application domains. The information subsequently available to the reasoning process is deliberately limited to model what an agent would actually be able to sense. The reasoning is therefore not a classical "all-knowing" strategy but uses these "sensed" facts obtained from the agents, combined with generic domain knowledge, to generate causal explanations of interactions. We present results from urban surveillance video.
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paper_title: Shape Based Object Classification for Automated Video Surveillance with Feature Selection

paper_content:

Object classification based on shape features for video surveillance has been a research problem for number of years. The object classification accuracy depends on the type of classifier and the extracted object features used for classification. Excellent classification accuracy can be obtained with an appropriate combination of the extracted features with a particular classifier. In this paper, we propose to use an online feature selection method which gives a good subset of features while the machine learns the classification task and use these selected features for object classification. This paper also explores the impact of different kinds of shape features on the object classification accuracy and the performance of different classifiers in a typical automated video surveillance application.
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paper_title: Online EM Algorithm for Background Subtraction

paper_content:

Abstract Gaussian mixture model is a popular model in background subtraction and efficient equations have been derived to update GMM parameters previously. In order to compute parameters more accurately while maintain constant computing time per frame, we apply online EM algorithm to update the parameters of Gaussian mixture models. To avoid computing the inverse of covariance matrix, we use isotropic matrix and the corresponding incremental EM equations are derived. Experiments demonstrate that online EM algorithm can give more accurate segment result than previous update equations.
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paper_title: Proposed framework of Intelligent Video Automatic Target Recognition System (IVATRs)

paper_content:

In this paper we have introduce a novel hybrid method framework for feature extraction using genetic algorithms in Automatic Target Recognition. In this paper we have given a complete framework and approach for making the system of Automatic Target Recognition System we suggest the name of IVATRs (Intelligent Video Automatic Target Recognition System). This framework will be helpful for making a bridge between the theoretical models of AI techniques and their implementations with hardware. During this study our objective is to design a hybrid mechanism for the currently existing ATRs techniques. This study will also lead us to the result of predicting the behavior of targeted objects and future machines.
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paper_title: Multiple Camera Tracking of Interacting and Occluded Human Motion

paper_content:

We propose a distributed, real-time computing platform for tracking multiple interacting persons in motion. To combat the negative effects of occlusion and articulated motion we use a multiview implementation, where each view is first independently processed on a dedicated processor. This monocular processing uses a predictor-corrector filter to weigh reprojections of three-dimensional (3-D) position estimates, obtained by the central processor, against observations of measurable image motion. The corrected state vectors from each view provide input observations to a Bayesian belief network, in the central processor, with a dynamic, multidimensional topology that varies as a function of scene content and feature confidence. The Bayesian net fuses independent observations from multiple cameras by iteratively resolving independency relationships and confidence levels within the graph, thereby producing the most likely vector of 3-D state estimates given the available data. To maintain temporal continuity, we follow the network with a layer of Kalman filtering that updates the 3-D state estimates. We demonstrate the efficacy of the proposed system using a multiview sequence of several people in motion. Our experiments suggest that, when compared with data fusion based on averaging, the proposed technique yields a noticeable improvement in tracking accuracy.
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paper_title: Image change detection algorithms: a systematic survey

paper_content:

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.
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paper_title: Dynamic Background Subtraction Using Spatial-Color Binary Patterns

paper_content:

In this paper, an efficient approach for background modeling and subtraction is proposed. It's based on a novel spatial-color feature extraction operator named spatial-color binary patterns(SCBP). As the name implies, features extracted by this operator include spatial texture and color information. In addition, a refine module is designed to refine the contour of moving objects. Using the proposed method, we improve the accuracy of subtracting the background and detecting moving objects in dynamic scenes. A data-driven model is used in our method. For each pixel, first, a histogram of SCBP is extracted from the circular egion, and then a model consist of several histograms is built. For a new observed frame, each pixel is labeled either background or foreground according to the matching degree between its SCBP histogram and its model, then the label is refined and finally the model of this pixel is updated. The proposed pproach is tested on challenging video sequences, which shows that the proposed method performs much better than several texture-based methods.
---
paper_title: Statistical Background Modeling: An Edge Segment Based Moving Object Detection Approach

paper_content:

We propose an edge segment based statistical background modeling algorithm and a moving edge detection framework for the detection of moving objects. We analyze the performance of the proposed segment based statistical background model with traditional pixel based, edge pixel based and edge segment based approaches. Existing edge based moving object detection algorithms fetches difficulty due to the change in background motion, object shape, illumination variation and noise. The proposed algorithm makes efficient use of statistical background model using the edge-segment structure. Experiments with natural image sequences show that our method can detect moving objects efficiently under the above mentioned environments.
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paper_title: Research on GMM Background Modeling and its Covariance Estimation

paper_content:

This paper analyzes the background modeling mechanism using Gaussian mixture model and the stability /plasticity dilemma in parameters estimation of GMM background model. To solve the slow convergence problem of Gaussian mean and covariance update formula given by Stauffer, a new updating strategy is proposed, which weighs the model adaptability and motion segmentation accuracy. Experiments show that the proposed algorithm improves the accuracy of modal learning and speed of covariance convergence.
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paper_title: Review and evaluation of commonly-implemented background subtraction algorithms

paper_content:

Locating moving objects in a video sequence is the first step of many computer vision applications. Among the various motion-detection techniques, background subtraction methods are commonly implemented, especially for applications relying on a fixed camera. Since the basic inter-frame difference with global threshold is often a too simplistic method, more elaborate (and often probabilistic) methods have been proposed. These methods often aim at making the detection process more robust to noise, background motion and camera jitter. In this paper, we present commonly-implemented background subtraction algorithms and we evaluate them quantitatively. In order to gauge performances of each method, tests are performed on a wide range of real, synthetic and semi-synthetic video sequences representing different challenges.
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paper_title: Real-time background subtraction for video surveillance: From research to reality

paper_content:

This paper reviews and evaluates performance of few common background subtraction algorithms which are medianbased, Gaussian-based and Kernel density-based approaches. These algorithms are tested using four sets of image sequences contributed by Wallflower datasets. They are the image sequences of different challenging environments that may reflect the real scenario in video surveillances. The performances of these approaches are evaluated in terms of processing speed, memory usage as well as object segmentation accuracy. The results demonstrate that Gaussian-based approach is the best approach for real-time applications, compromising between accuracy and computational time. Besides, this paper may provide a better understanding of algorithm behaviours implemented in different situation for real-time video surveillance applications.
---
paper_title: ViBe: A Universal Background Subtraction Algorithm for Video Sequences

paper_content:

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.
---
paper_title: Moving object detection in spatial domain using background removal techniques -- State-of-art

paper_content:

Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal algorithms in the literature, most of them follow a simple flow diagram, passing through four major steps, which are pre-processing, background modelling, foreground de- tection and data validation. In this paper, we survey many existing schemes in the literature of background removal, sur- veying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized. We also survey how to measure the performance of any moving object detection algorithm, whether the ground truth data is available or not, presenting per- formance metrics commonly used in both cases.
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paper_title: Multiclass object classification for real-time video surveillance systems

paper_content:

Object classification in video is an important factor for improving the reliability of various automatic applications in video surveillance systems, as well as a fundamental feature for advanced applications, such as scene understanding. Despite extensive research, existing methods exhibit relatively moderate classification accuracy when tested on a large variety of real-world scenarios, or do not obey the real-time constraints of video surveillance systems. Moreover, their performance is further degraded in multi-class classification problems. We explore multi-class object classification for real-time video surveillance systems and propose an approach for classifying objects in both low and high resolution images (human height varies from a few to tens of pixels) in varied real-world scenarios. Firstly, we present several features that jointly leverage the distinction between various classes. Secondly, we provide a feature-selection procedure based on entropy gain, which screens out superfluous features. Experiments, using various classification techniques, were performed on a large and varied database consisting of ~29,000 object instances extracted from 140 different real-world indoor and outdoor, near-field and far-field scenes having various camera viewpoints, which capture a large variety of object appearances under real-world environmental conditions. The insight raised from the experiments is threefold: the efficiency of our feature set in discriminating between classes, the performance improvement when using the feature selection method, and the high classification accuracy obtained on our real-time system on both DSP (TMS320C6415-6E3, 600MHz) and PC (Quad Core Intel(R) Xeon(R) E5310, 2x4MB Cache, 1.60GHz, 1066MHz) platforms.
---
paper_title: Real-time Object Classification in Video Surveillance Based on Appearance Learning

paper_content:

Classifying moving objects to semantically meaningful categories is important for automatic visual surveillance. However, this is a challenging problem due to the factors related to the limited object size, large intra-class variations of objects in a same class owing to different viewing angles and lighting, and real-time performance requirement in real-world applications. This paper describes an appearance-based method to achieve real-time and robust objects classification in diverse camera viewing angles. A new descriptor, i.e., the multi-block local binary pattern (MB-LBP), is proposed to capture the large-scale structures in object appearances. Based on MB-LBP features, an adaBoost algorithm is introduced to select a subset of discriminative features as well as construct the strong two-class classifier. To deal with the non-metric feature value of MB-LBP features, a multi-branch regression tree is developed as the weak classifiers of the boosting. Finally, the error correcting output code (ECOC) is introduced to achieve robust multi-class classification performance. Experimental results show that our approach can achieve real-time and robust object classification in diverse scenes.
---
paper_title: Shape Based Object Classification for Automated Video Surveillance with Feature Selection

paper_content:

Object classification based on shape features for video surveillance has been a research problem for number of years. The object classification accuracy depends on the type of classifier and the extracted object features used for classification. Excellent classification accuracy can be obtained with an appropriate combination of the extracted features with a particular classifier. In this paper, we propose to use an online feature selection method which gives a good subset of features while the machine learns the classification task and use these selected features for object classification. This paper also explores the impact of different kinds of shape features on the object classification accuracy and the performance of different classifiers in a typical automated video surveillance application.
---
paper_title: Adaptive mean-shift for automated multi object tracking

paper_content:

Mean-shift tracking plays an important role in computer vision applications because of its robustness, ease of implementation and computational efficiency. In this study, a fully automatic multiple-object tracker based on mean-shift algorithm is presented. Foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the object trackers and also used as a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to converge for the new location of the object. By using foreground detection, new objects entering to the field of view and objects that are leaving the scene could be detected. Trackers are automatically refreshed to solve the potential problems that may occur because of the changes in objects' size, shape, to handle occlusion-split between the tracked objects and to detect newly emerging objects as well as objects that leave the scene. Using a shadow removal method increases the tracking accuracy. As a result, a method that remedies problems of mean-shift tracking and presents an easy to implement, robust and efficient tracking method that can be used for automated static camera video surveillance applications is proposed. Additionally, it is shown that the proposed method is superior to the standard mean-shift.
---
paper_title: Bayesian visual surveillance: A model for detecting and tracking a variable number of moving objects

paper_content:

An automatic detection and tracking framework for visual surveillance is proposed, which is able to handle a variable number of moving objects. Video object detectors generate an unordered set of noisy, false, missing, split, and merged measurements that make extremely complex the tracking task. Especially challenging are split detections (one object is split into several measurements) and merged detections (several objects are merged into one detection). Few approaches address this problem directly, and the existing ones use heuristics methods, or assume a known number of objects, or are not suitable for on-line applications. In this paper, a Bayesian Visual Surveillance Model is proposed that is able to manage undesirable measurements. Particularly, split and merged measurements are explicitly modeled by stochastic processes. Inference is accurately performed through a particle filtering approach that combines ancestral and MCMC sampling. Experimental results have shown a high performance of the proposed approach in real situations.
---
paper_title: Multi-Feature Fusion Based Object Detecting and Tracking

paper_content:

A new approach is proposed to detect and track the moving object. The affine motion model and the non-parameter distribution model are utilized to represent the object firstly. Then the motion region of the object is detected by background difference while Kalman filter estimating its affine motion in next frame. Center association and mean shift are adopted to obtain the observation values. Finally, the distance variance and scale variance between the estimated and detected regions are used to fuse the observation values to acquire the measurement value. To correct fusion errors, the observable edges are employed. Experimental results show that the new method can successfully track the object under such case as merging, splitting, scale variation and scene noise.
---
paper_title: Object Tracking and Detecting Based on Adaptive Background Subtraction

paper_content:

Abstract A tracking algorithm based on adaptive background subtraction about the video detecting and tracking moving objects is presented in this paper. Firstly, we use median filter to achieve the background image of the video and denoise the sequence of video. Then we use adaptive background subtraction algorithm to detect and track the moving objects. Adaptive background updating is also realized in this paper. Finally, we improve the accuracy of tracking through open operation. The simulation results by MATLAB show that the adaptive background subtraction is useful in both detecting and tracking moving objects, and background subtraction algorithm runs more quickly.
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paper_title: Object tracking by detection for video surveillance systems based on modified codebook foreground detection and particle filter

paper_content:

In this paper, a novel approach is proposed to achieve the multi-object tracking in video surveillance system using a combination of tracking by detection method. For the foreground objects detection part, we implement a modified codebook model. First, the block-based model upgrades the pixel-based codebook model to block level, thus improving the processing speed and reducing memory. Moreover, by adding the orientation and magnitude of the block gradient, the codebook model contains not only information of color, but also the texture feature in order to further reduce noises and refine more entire foreground regions. For the tracking aspect, we further utilize the data from the foreground detection that a color-edgetexture histogram is used by calculate the local binary pattern of the edge of the foreground objects which could have a good performance in describing the shape and texture of the objects. Finally, occlusion solutions strategies are applies to order to overcome the occlusion problems during tracking. Experimental results on different data sets prove that our method has better performance and good real-time ability.
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paper_title: Tracking Pedestrians Using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes

paper_content:

Tracking pedestrians is a vital component of many computer vision applications, including surveillance, scene understanding, and behavior analysis. Videos of crowded scenes present significant challenges to tracking due to the large number of pedestrians and the frequent partial occlusions that they produce. The movement of each pedestrian, however, contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents over the entire video) that exhibits an underlying spatially and temporally varying structured pattern. In this paper, we present a novel Bayesian framework for tracking pedestrians in videos of crowded scenes using a space-time model of the crowd motion. We represent the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video. Using this unique representation, we predict the next local spatio-temporal motion pattern a tracked pedestrian will exhibit based on the observed frames of the video. We then use this prediction as a prior for tracking the movement of an individual in videos of extremely crowded scenes. We show that our approach of leveraging the crowd motion enables tracking in videos of complex scenes that present unique difficulty to other approaches.
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paper_title: Research on Detection and Tracking of Moving Target in Intelligent Video Surveillance

paper_content:

Now the visual surveillance system has been studied extensively, and has made great progress, but many surveillance systems are only applied in specific sites, and the level of intelligence is not high. Intelligent video surveillance system mainly carries out research on techniques of detection and tracking of moving targets, which are very important for detection and understanding of abnormal behaviors, so the effect of moving detection directly affects the overall effect of video surveillance systems, moving detection of intelligent visual surveillance is mainly target classification, tracking, detection, behavior recognition and so on. Detection and tracking of moving targets in surveillance applications has greatly improved intelligence, accuracy and reliability of video surveillance and has greatly reduced the burden on staff.
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paper_title: Automatic human activity recognition in video using background modeling and spatio-temporal template matching based technique

paper_content:

Human activity recognition is a challenging area of research because of its various potential applications in visual surveillance. A spatio-temporal template matching based approach for activity recognition is proposed in this paper. We model the background in a scene using a simple statistical model and extract the foreground objects in a scene. Spatio-temporal templates are constructed using the motion history images (MHI) and object shape information for different human activities in a video like walking, standing, bending, sleeping and jumping. Experimental results show that the method can recognize these multiple activities for multiple objects with accuracy and speed.
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paper_title: Eigenspace-based fall detection and activity recognition from motion templates and machine learning

paper_content:

Automatic recognition of anomalous human activities and falls in an indoor setting from video sequences could be an enabling technology for low-cost, home-based health care systems. Detection systems based upon intelligent computer vision software can greatly reduce the costs and inconveniences associated with sensor based systems. In this paper, we propose such a software based upon a spatio-temporal motion representation, called Motion Vector Flow Instance (MVFI) templates, that capture relevant velocity information by extracting the dense optical flow from video sequences of human actions. Automatic recognition is achieved by first projecting each human action video sequence, consisting of approximately 100 images, into a canonical eigenspace, and then performing supervised learning to train multiple actions from a large video database. We show that our representation together with a canonical transformation with PCA and LDA of image sequences provides excellent action discrimination. We also demonstrate that by including both the magnitude and direction of the velocity into the MVFI, sequences with abrupt velocities, such as falls, can be distinguished from other daily human action with both high accuracy and computational efficiency. As an added benefit, we demonstrate that, once trained, our method for detecting falls is robust and we can attain real-time performance.
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paper_title: Event detection and recognition using histogram of oriented gradients and hidden markov models

paper_content:

This paper presents an approach for object detection and event recognition in video surveillance scenarios. The proposed system utilizes a Histogram of Oriented Gradients (HOG) method for object detection, and a Hidden Markov Model (HMM) for capturing the temporal structure of the features. Decision making is based on the understanding of objects motion trajectory and the relationships between objects' movement and events. The proposed method is applied to recognize events from the public PETS and i-LIDS datasets, which include vehicle events such as U-turns and illegal parking, as well as abandoned luggage recognition established by set of rules. The effectiveness of the proposed solution is demonstrated through extensive experimentation.
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paper_title: A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

paper_content:

Having a good automatic anomalous human behaviour ::: detection is one of the goals of smart surveillance ::: systems’ domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to correctly understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context; (b)It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to considering knowledge learned from the relevant context only.
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paper_title: Video Behavior Profiling for Anomaly Detection

paper_content:

This paper aims to address the problem of modeling video behavior captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior profiling and online anomaly sampling/detection without any manual labeling of the training data set. The framework consists of the following key components: 1) A compact and effective behavior representation method is developed based on discrete-scene event detection. The similarity between behavior patterns are measured based on modeling each pattern using a Dynamic Bayesian Network (DBN). 2) The natural grouping of behavior patterns is discovered through a novel spectral clustering algorithm with unsupervised model selection and feature selection on the eigenvectors of a normalized affinity matrix. 3) A composite generative behavior model is constructed that is capable of generalizing from a small training set to accommodate variations in unseen normal behavior patterns. 4) A runtime accumulative anomaly measure is introduced to detect abnormal behavior, whereas normal behavior patterns are recognized when sufficient visual evidence has become available based on an online Likelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. The effectiveness and robustness of our approach is demonstrated through experiments using noisy and sparse data sets collected from both indoor and outdoor surveillance scenarios. In particular, it is shown that a behavior model trained using an unlabeled data set is superior to those trained using the same but labeled data set in detecting anomaly from an unseen video. The experiments also suggest that our online LRT-based behavior recognition approach is advantageous over the commonly used Maximum Likelihood (ML) method in differentiating ambiguities among different behavior classes observed online.
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paper_title: Bayesian multi-camera surveillance

paper_content:

The task of multicamera surveillance is to reconstruct the paths taken by all moving objects that are temporally visible from multiple non-overlapping cameras. We present a Bayesian formalization of this task, where the optimal solution is the set of object paths with the highest posterior probability given the observed data. We show how to efficiently approximate the maximum a posteriori solution by linear programming and present initial experimental results.
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paper_title: Algorithms for Cooperative Multisensor Surveillance

paper_content:

The Video Surveillance and Monitoring (VSAM) team at Carnegie Mellon University (CMU) has developed an end-to-end, multicamera surveillance system that allows a single human operator to monitor activities in a cluttered environment using a distributed network of active video sensors. Video understanding algorithms have been developed to automatically detect people and vehicles, seamlessly track them using a network of cooperating active sensors, determine their three-dimensional locations with respect to a geospatial site model, and present this information to a human operator who controls the system through a graphical user interface. The goal is to automatically collect and disseminate real-time information to improve the situational awareness of security providers and decision makers. The feasibility of real-time video surveillance has been demonstrated within a multicamera testbed system developed on the campus of CMU. This paper presents an overview of the issues and algorithms involved in creating this semiautonomous, multicamera surveillance system.
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paper_title: Multiple Camera Tracking of Interacting and Occluded Human Motion

paper_content:

We propose a distributed, real-time computing platform for tracking multiple interacting persons in motion. To combat the negative effects of occlusion and articulated motion we use a multiview implementation, where each view is first independently processed on a dedicated processor. This monocular processing uses a predictor-corrector filter to weigh reprojections of three-dimensional (3-D) position estimates, obtained by the central processor, against observations of measurable image motion. The corrected state vectors from each view provide input observations to a Bayesian belief network, in the central processor, with a dynamic, multidimensional topology that varies as a function of scene content and feature confidence. The Bayesian net fuses independent observations from multiple cameras by iteratively resolving independency relationships and confidence levels within the graph, thereby producing the most likely vector of 3-D state estimates given the available data. To maintain temporal continuity, we follow the network with a layer of Kalman filtering that updates the 3-D state estimates. We demonstrate the efficacy of the proposed system using a multiview sequence of several people in motion. Our experiments suggest that, when compared with data fusion based on averaging, the proposed technique yields a noticeable improvement in tracking accuracy.
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Title: A Survey Of Activity Recognition And Understanding The Behavior In Video Surveillance

Section 1: INTRODUCTION
Description 1: This section introduces the study's objective, context, and overview of visual surveillance technologies and their applications.

Section 2: SURROUNDING OF MODEL
Description 2: This section discusses background modeling techniques and their use in detecting moving objects in video surveillance.

Section 3: OBJECT REPRESENTATION AND CLASSIFICATION
Description 3: This section covers methods for classifying and representing objects detected in surveillance footage.

Section 4: OBJECT TRACKING
Description 4: This section elaborates on techniques and challenges associated with tracking objects across video frames.

Section 5: DESCRIPTION AND BEHAVIOUR UNDERSTANDING
Description 5: This section details methods for understanding and recognizing human actions and behaviors from video data.

Section 6: DATA FUSION
Description 6: This section explains the importance of data fusion in handling occlusion and ensuring continuous object tracking.

Section 7: CONCLUSION
Description 7: This section summarizes the current state and future directions of automated video surveillance systems, focusing on behavior analysis and activity recognition.