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Background modeling and foreground detection for video surveillance

Author: Thierry Bouwmans; Fatih Porikli; Benjamin Höferlin; Antoine Vacavant
Publisher: Boca Raton, Florida : CRC Press, [2015]
Edition/Format:   Print book : EnglishView all editions and formats
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  Read more...
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Document Type: Book
All Authors / Contributors: Thierry Bouwmans; Fatih Porikli; Benjamin Höferlin; Antoine Vacavant
ISBN: 9781482205374 1482205378
OCLC Number: 859584240
Description: 1 volume (various pagings) : illustrations (some color) ; 27 cm
Contents: Introduction and BackgroundTraditional Approaches in Background Modeling for Static Cameras Thierry Bouwmans Recent Approaches in Background Modeling for Static Cameras Thierry Bouwmans Background Model Initialization for Static Cameras Lucia Maddalena and Alfredo Petrosino Background Subtraction for Moving Cameras Ahmed Elgammal and Ali Elqursh Traditional and Recent ModelsStatistical Models for Background Subtraction Ahmed Elgammal Non-Parametric Background Segmentation with Feedback and Dynamic Controllers Philipp Tiefenbacher, Martin Hofmann, and Gerhard Rigoll ViBe: A Disruptive Method for Background Subtraction Marc Van Droogenbroeck and Olivier Barnich Online Learning by Stochastic Approximation for Background Modeling Ezequiel Lopez-Rubio and Rafael M. Luque-Baena Sparsity-Driven Background Modeling and Foreground Detection Jun-zhou Huang, Chen Chen, and Xinyi Cui Robust Detection of Moving Objects through Rough Set Theory Framework Pojala Chiranjeevi and Somnath Sengupta Applications in Video SurveillanceBackground Learning with Support Vectors: Efficient Foreground Detection and Tracking for Automated Visual Surveillance Alireza Tavakkoli, Mircea Nicolescu, Junxian Wang, and George Bebis Incremental Learning of an Infinite Beta-Liouville Mixture Model for Video Background Subtraction Wentao Fan and Nizar Bouguila Spatio-Temporal Background Models for Object Detection Satoshi Yoshinaga, Yosuke Nonaka, Atsushi Shimada, Hajime Nagahara, and Rin-ichiro Taniguchi Background Modeling and Foreground Detection for Maritime Video Surveillance Domenico Bloisi Hierarchical Scene Model for Spatial-color Mixture of Gaussians Christophe Gabard, Catherine Achard, and Laurent LucatOnline Robust Background Modeling via Alternating Grassmannian Optimization Jun He, Laura Balzano, and Arthur Szlam Sensors, Hardware, and ImplementationsUbiquitous Imaging (Light, Thermal, Range, Radar) Sensors for People Detection: An Overview Zoran Zivkovic RGB-D Cameras for Background-Foreground Segmentation Massimo Camplani and Luis Salgado Non-Parametric GPU Accelerated Background Modeling of Complex Scenes Ashutosh Morde and Sadiye Guler GPU Implementation for Background-Foreground-Separation via Robust PCA and Robust Subspace Tracking Clemens Hage, Florian Seidel, and Martin Kleinsteuber Background Subtraction on Embedded Hardware Enrique J. Fernandez-Sanchez, Rafael Rodriguez-Gomez, Javier Diaz, and Eduardo Ros Resource-Efficient Salient Foreground Detection for Embedded Smart Cameras Senem Velipasalar and Mauricio Casares Benchmarking and EvaluationBGS Library: A Library Framework for Algorithms Evaluation in Foreground/Background Segmentation Andrews Sobral and Thierry Bouwmans Overview and Benchmarking of Motion Detection Methods Pierre-Marc Jodoin, Sebastien Pierard, Yi Wang, and Marc Van Droogenbroeck Evaluation of Background Models with Synthetic and Real Data Antoine Vacavant, Laure Tougne, Thierry Chateau, and Lionel RobinaultIndex
Responsibility: edited by Thierry Bouwmans Fatih Porikli, Benjamin Höferlin, Antoine Vacavant.

Abstract:

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.

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"... a very interesting, extensive, and up-to-date book about various aspects related to background subtraction for video surveillance systems. It successfully provides a complete overview and an Read more...

 
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