High Definition Video Segmentation Techniques-A Review
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paper_title: Comparison of Background Subtraction Methods for a Multimedia Learning Space.

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A press-formed article has a quenched area and a required strength distribution. The quenched area has a hardness distribution exhibiting a hardness transition which corresponds to the required strength distribution. The press-formed article may include one end portion, the other end portion, and a central area disposed between the one end portion and the other end portion. The quenched area is extended from the one end portion and to the other end portion. The hardness distribution of the quenched area exhibits a condition that hardness is the highest in the central area in such a manner that hardness is gradually decreased from the central area to the one end portion and to the other end portion.
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paper_title: Vision modules for a multi-sensory bridge monitoring approach

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This work presents the use of multiple sensor modalities in order to perform traffic analysis for health monitoring of transportation infrastructure. In particular, testbeds containing video and seismic sensors giving complementary information about vehicles are described. Computer vision algorithms are used to detect and track the vehicles and extract their properties. This information is combined with the data from seismic sensors for robust classification of vehicles. Experimental results obtained with our testbeds are described.
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paper_title: Fuzzy Running Average and Fuzzy Background Subtraction: Concepts and Application

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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.
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paper_title: Free-viewpoint video of human actors

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In free-viewpoint video, the viewer can interactively choose his viewpoint in 3-D space to observe the action of a dynamic real-world scene from arbitrary perspectives. The human body and its motion plays a central role in most visual media and its structure can be exploited for robust motion estimation and efficient visualization. This paper describes a system that uses multi-view synchronized video footage of an actor's performance to estimate motion parameters and to interactively re-render the actor's appearance from any viewpoint.The actor's silhouettes are extracted from synchronized video frames via background segmentation and then used to determine a sequence of poses for a 3D human body model. By employing multi-view texturing during rendering, time-dependent changes in the body surface are reproduced in high detail. The motion capture subsystem runs offline, is non-intrusive, yields robust motion parameter estimates, and can cope with a broad range of motion. The rendering subsystem runs at real-time frame rates using ubiquous graphics hardware, yielding a highly naturalistic impression of the actor. The actor can be placed in virtual environments to create composite dynamic scenes. Free-viewpoint video allows the creation of camera fly-throughs or viewing the action interactively from arbitrary perspectives.
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paper_title: Extracting Roadway Background Image: a Mode-Based Approach

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Traffic monitoring cameras are widely installed on streets and freeways in U.S. metropolitan areas. Video images captured from these video cameras can be used to extract many valuable traffic parameters through video image processing. A popular way to capture traffic data is to compare the current traffic images with the background image, which contains no vehicles or other moving objects, just background such as pavement. Once the moving vehicle images are separated from the background image, measurements of their number, speed, and so on can be obtained. Typically, background images are extracted from a video stream through image processing because it may be hard to find a frame without any vehicles for normal traffic streams on urban streets. This paper introduces a new method that can quickly extract the background image from traffic video streams for both freeways and intersections in a variety of prevailing traffic conditions. This method has been tested with field data, and the results are promising.
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paper_title: Human Body Model Acquisition and Tracking Using Voxel Data

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We present an integrated system for automatic acquisition of the human body model and motion tracking using input from multiple synchronized video streams. The video frames are segmented and the 3D voxel reconstructions of the human body shape in each frame are computed from the foreground silhouettes. These reconstructions are then used as input to the model acquisition and tracking algorithms. ::: ::: The human body model consists of ellipsoids and cylinders and is described using the twists framework resulting in a non-redundant set of model parameters. Model acquisition starts with a simple body part localization procedure based on template fitting and growing, which uses prior knowledge of average body part shapes and dimensions. The initial model is then refined using a Bayesian network that imposes human body proportions onto the body part size estimates. The tracker is an extended Kalman filter that estimates model parameters based on the measurements made on the labeled voxel data. A voxel labeling procedure that handles large frame-to-frame displacements was designed resulting in very robust tracking performance. ::: ::: Extensive evaluation shows that the system performs very reliably on sequences that include different types of motion such as walking, sitting, dancing, running and jumping and people of very different body sizes, from a nine year old girl to a tall adult male.
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paper_title: Pfinder: real-time tracking of the human body

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Pfinder is a real-time system for tracking and interpretation of people. It runs on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to segment a person from a background scene, and implements heuristics which can find and track people's head and hands in a wide range of viewing conditions. Pfinder produces a real- time representation of a user useful for applications such as wireless interfaces, video databases, and low-bandwidth coding, without cumbersome wires or attached sensors.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Wallflower: principles and practice of background maintenance

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Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three-component system for background maintenance: the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and swaps in better approximations of the background. We compare our system with 8 other background subtraction algorithms. Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur. Finally, we analyze the experimental results and propose normative principles for background maintenance.
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paper_title: Background subtraction techniques: a review

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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.
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paper_title: Bayesian background modeling for foreground detection

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We propose a Bayesian learning method to capture the background statistics of a dynamic scene. We model each pixel as a set of layered normal distributions that compete with each other. Using a recursive Bayesian learning mechanism, we estimate not only the mean and variance but also the probability distribution of the mean and covariance of each model. This learning algorithm preserves the multimodality of the background process and is capable of estimating the number of required layers to represent each pixel.
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paper_title: Moving object detection in spatial domain using background removal techniques -- State-of-art

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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: ASIC and FPGA Implementation of the Gaussian Mixture Model Algorithm for Real-Time Segmentation of High Definition Video

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Background identification is a common feature in many video processing systems. This paper proposes two hardware implementations of the OpenCV version of the Gaussian mixture model (GMM), a background identification algorithm. The implemented version of the algorithm allows a fast initialization of the background model while an innovative, hardware-oriented, formulation of the GMM equations makes the proposed circuits able to perform real-time background identification on high definition (HD) video sequences with frame size 1920 × 1080. The first of the two circuits is designed with commercial field-programmable gate-array (FPGA) devices as target. When implemented on Virtex6 vlx75t, the proposed circuit process 91 HD fps (frames per second) and uses 3% of FPGA logic resources. The second circuit is oriented to the implementation in UMC-90 nm CMOS standard cell technology, and is proposed in two versions. Both versions can process at a frame rate higher than 60 HD fps. The first version uses the constant voltage scaling technique to provide a low power implementation. It provides silicon area occupation of 28847 μm2 and energy dissipation per pixel of 15.3 pJ/pixel. The second version is designed to reduce silicon area utilization and occupies 21847 μm2 with an energy dissipation of 49.4 pJ/pixel.
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Title: High Definition Video Segmentation Techniques - A Review

Section 1: INTRODUCTION
Description 1: This section introduces background modeling techniques and their applications in video surveillance, optical motion capture, and multimedia. It highlights the challenges in modeling the background and the various methods developed to address robustness and adaptation issues.

Section 2: VIDEO SEGMENTATION ALGORITHMS
Description 2: This section discusses various adaptive background models for video segmentation, including the challenges faced by these models in dealing with multi-modal background distribution and real-time processing requirements.

Section 3: BACKGROUND MODELING USING MIXTURE OF GAUSSIANS
Description 3: This section explains the use of mixture of Gaussians for background modeling, particularly in traffic surveillance systems. It reviews the methodology and the algorithm proposed by Stauffer and Grimson to manage multimodal background distributions.

Section 4: TRAINABLE SEGMENTATION
Description 4: This section describes trainable segmentation methods using neural networks, particularly emphasizing Pulse-Coupled Neural Networks (PCNNs). It details how these methods can be used for image processing applications, including image segmentation and noise reduction.

Section 5: CONCLUSION
Description 5: This section summarizes the reviewed video segmentation algorithms and suggests that trainable segmentation methods can overcome the limitations of Gaussian mixture models in FPGA implementations, thereby enhancing real-time processing capabilities.