Background Modeling and Foreground Detection for Video Surveillance

Thierry Bouwmans, Fatih Porikli, Benjamin Höferlin, Antoine Vacavant

© 2014 - Chapman and Hall/CRC
Published July 25, 2014
Reference - 631 Pages - 10 Color & 280 B/W Illustrations
ISBN 9781482205374 - CAT# K21446

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  • Brings together the methods, practical implementations, and evaluation practices of background modeling and foreground detection in one resource
  • Includes an introduction for beginners that covers traditional and recent approaches for both static and moving cameras
  • Describes statistical models, clustering models, neural networks, and fuzzy models
  • Discusses various strategies to deal with dynamic backgrounds and illumination changes, such as automatic feature selection, hierarchical models, and GPU implementations of methods
  • Offers the datasets and code used in the book on a supplementary website


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.


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