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2014 | OriginalPaper | Chapter

Background Subtraction: Theory and Practice

Author : Ahmed Elgammal

Published in: Wide Area Surveillance

Publisher: Springer Berlin Heidelberg

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Abstract

Background subtraction is a widely-used concept utilized to detect moving objects in videos taken from a static camera. In the last two decades, several algorithms have been developed for background subtraction and were used in various important applications such as visual surveillance, sports video analysis, motion capture, etc. Various statistical approaches have been proposed to model scene backgrounds. In this chapter we review the concept and the practice in background subtraction. We discuss several basic statistical background subtraction models, including parametric Gaussian models and nonparametric models. We discuss the issue of shadow suppression, which is essential for human motion analysis applications. We also discuss approaches and tradeoffs for background maintenance. We also point out many of the recent developments in the background subtraction paradigm.

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next chapter Moving Cast Shadows Detection Methods for Video Surveillance Applications

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Title: Background Subtraction: Theory and Practice
Author: Ahmed Elgammal
Publisher: Springer Berlin Heidelberg
Book: Wide Area Surveillance
Print ISBN: 978-3-642-37840-9

Electronic ISBN: 978-3-642-37841-6

Copyright Year: 2014
DOI: https://doi.org/10.1007/8612_2012_1

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