Cover image for Background modeling and foreground detection for video surveillance
Title:
Background modeling and foreground detection for video surveillance
Publication Information:
Boca Raton, FL : Chapman and Hall/CRC, 2015
Physical Description:
1 volume (various pagings) : illustrations (some color) ; 26 cm.
ISBN:
9781482205374

Available:*

Library
Item Barcode
Call Number
Material Type
Item Category 1
Status
Searching...
30000010341252 TK6680.3 B33 2015 Open Access Book Book
Searching...

On Order

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

Thierry BouwmansThierry BouwmansLucia Maddalena and Alfredo PetrosinoAhmed Elgammal and All ElqurshAhmed ElgaminalPhilipp Ticfenbacher and Martin Hofmann and Gerhard RigollMarc Van Droogenbroeck and Olivier BarnichEzequiel López-Rubio and Rafael M. Luquc-BaenaJunzhou Huang and Chen Chen and Xinyi CiiiPojala Chiranjeevi and Somnath SenguptaAlireza Tavakkoli and Mircea Nicolescu and Junxian Wang and George BebisWentao Fan and Nizar BouguilaSatoshi Yoshinaga and Yosuke Nonaka and Atsushi Shimada and Hajhne Nagahara and Rin-ichiro TaniguchiDomenico BloisiChristophe Gabard and Catherine Achard and Laurent LucatJun He and Laura Balzano and Arthur SzlamZoran ZivkovicMassimo Camplani and Luis SalgadoAshutosh Morde and Sadiye GulerClemens Hage and Flohan Seidel and Martin KleinsteuberEnrique J. Fernandez-Sanchez and Rafael Rodriguez-Gomez and Javier Diaz and Eduardo RosSenem Velipasalar and Mauricio CasaresAndrews Sobral and Thierry BouwmausPierre-Marc Jodoin and Sébastien Piérard and Yi Wang and Marc Van DroogenbroeckAntoine Vacavant and Laure Tougne and Thierry Chateau and Lionel Robinault
Prefacep. ix
About the Editorsp. xv
List of Contributorsp. xvii
Part I Introduction and Background
1 Traditional Approaches in Background Modeling for Static Camerasp. 1
2 Recent Approaches in Background Modeling for Static Camerasp. 2
3 Background Model Initialization for Static Camerasp. 3
4 Background Subtraction for Moving Camerasp. 4
Part II Traditional and Recent Models
5 Statistical Models for Background Subtractionp. 5
6 Non-parametric Background Segmentation with Feedback and Dynamic Controllersp. 6
7 ViBe: A Disruptive Method for Background Subtractionp. 7
8 Online Learning by Stochastic Approximation for Background Modelingp. 8
9 Sparsity Driven Background Modeling and Foreground Detectionp. 9
10 Robust Detection of Moving Objects through Rough Set Theory Frameworkp. 10
Part III Applications in Video Surveillance
11 Background Learning with Support Vectors: Efficient Foreground Detection and Tracking for Automated Visual Surveillancep. 11
12 Incremental Learning of an Infinite Beta-Liouville Mixture Model for Video Background Subtractionp. 12
13 Spatio-temporal Background Models for Object Detectionp. 13
14 Background Modeling and Foreground Detection for Maritime Video Surveillancep. 14
15 Hierarchical Scene Model for Spatial-color Mixture of Gaussiansp. 15
16 Online Robust Background Modeling via Alternating Grassraannian Optimizationp. 16
Part IV Sensors, Hardware and Implementations
17 Ubiquitous Imaging (Light, Thermal, Range, Radar) Sensors for People Detection: An Overviewp. 17
18 RGB-D Cameras for Background-Foreground Segmentationp. 18
19 Non-Parametric GPU Accelerated Background Modeling of Complex Scenesp. 19
20 GPU Implementation for Background-Foreground-Separation via Robust PCA and Robust Subspace Trackingp. 20
21 Background Subtraction on Embedded Hardwarep. 21
22 Resource-efficient Salient Foreground Detection for Embedded Smart Camerasp. 22
Part V Benchmarking and Evaluation
23 BGS Library: A Library Framework for Algorithms Evaluation in Foreground/Background Segmentationp. 23
24 Overview and Benchmarking of Motion Detection Methodsp. 24
25 Evaluation of Background Models with Synthetic and Real Datap. 25
Indexp. I