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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000010341252 | TK6680.3 B33 2015 | Open Access Book | Book | Searching... |
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Summary
Summary
Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.
Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection. Leaders in the field address a wide range of challenges, including camera jitter and background subtraction.
The book presents the top methods and algorithms for detecting moving objects in video surveillance. It covers statistical models, clustering models, neural networks, and fuzzy models. It also addresses sensors, hardware, and implementation issues and discusses the resources and datasets required for evaluating and comparing background subtraction algorithms. The datasets and codes used in the text, along with links to software demonstrations, are available on the book¿s website.
A one-stop resource on up-to-date models, algorithms, implementations, and benchmarking techniques, this book helps researchers and industry developers understand how to apply background models and foreground detection methods to video surveillance and related areas, such as optical motion capture, multimedia applications, teleconferencing, video editing, and human¿computer interfaces. It can also be used in graduate courses on computer vision, image processing, real-time architecture, machine learning, or data mining.
Table of Contents
Preface | p. ix |
About the Editors | p. xv |
List of Contributors | p. xvii |
Part I Introduction and Background | |
1 Traditional Approaches in Background Modeling for Static Cameras | p. 1 |
2 Recent Approaches in Background Modeling for Static Cameras | p. 2 |
3 Background Model Initialization for Static Cameras | p. 3 |
4 Background Subtraction for Moving Cameras | p. 4 |
Part II Traditional and Recent Models | |
5 Statistical Models for Background Subtraction | p. 5 |
6 Non-parametric Background Segmentation with Feedback and Dynamic Controllers | p. 6 |
7 ViBe: A Disruptive Method for Background Subtraction | p. 7 |
8 Online Learning by Stochastic Approximation for Background Modeling | p. 8 |
9 Sparsity Driven Background Modeling and Foreground Detection | p. 9 |
10 Robust Detection of Moving Objects through Rough Set Theory Framework | p. 10 |
Part III Applications in Video Surveillance | |
11 Background Learning with Support Vectors: Efficient Foreground Detection and Tracking for Automated Visual Surveillance | p. 11 |
12 Incremental Learning of an Infinite Beta-Liouville Mixture Model for Video Background Subtraction | p. 12 |
13 Spatio-temporal Background Models for Object Detection | p. 13 |
14 Background Modeling and Foreground Detection for Maritime Video Surveillance | p. 14 |
15 Hierarchical Scene Model for Spatial-color Mixture of Gaussians | p. 15 |
16 Online Robust Background Modeling via Alternating Grassraannian Optimization | p. 16 |
Part IV Sensors, Hardware and Implementations | |
17 Ubiquitous Imaging (Light, Thermal, Range, Radar) Sensors for People Detection: An Overview | p. 17 |
18 RGB-D Cameras for Background-Foreground Segmentation | p. 18 |
19 Non-Parametric GPU Accelerated Background Modeling of Complex Scenes | p. 19 |
20 GPU Implementation for Background-Foreground-Separation via Robust PCA and Robust Subspace Tracking | p. 20 |
21 Background Subtraction on Embedded Hardware | p. 21 |
22 Resource-efficient Salient Foreground Detection for Embedded Smart Cameras | p. 22 |
Part V Benchmarking and Evaluation | |
23 BGS Library: A Library Framework for Algorithms Evaluation in Foreground/Background Segmentation | p. 23 |
24 Overview and Benchmarking of Motion Detection Methods | p. 24 |
25 Evaluation of Background Models with Synthetic and Real Data | p. 25 |
Index | p. I |