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Background subtraction :theory and p...

Elgammal, Ahmed,

Background subtraction :theory and practice /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Background subtraction :/ Ahmed Elgammal
其他題名:	theory and practice /
作者:	Elgammal, Ahmed,
面頁冊數:	1 online resource (xvi, 67 pages) :illustrations :
提要註:	Background subtraction is a widely used concept for detection of moving objects in videos. In the last two decades there has been a lot of development in designing algorithms for background subtraction, as well as wide use of these algorithms in various important applications, such as visual surveillance, sports video analysis, motion capture, etc. Various statistical approaches have been proposed to model scene backgrounds. The concept of background subtraction also has been extended to detect objects from videos captured from moving cameras. This book reviews the concept and practice of background subtraction. We discuss several traditional statistical background subtraction models, including the widely used parametric Gaussian mixture models and non-parametric models. We also discuss the issue of shadow suppression, which is essential for human motion analysis applications. This book discusses approaches and tradeoffs for background maintenance
標題:	Image processing - Digital techniques -
電子資源:	http://portal.igpublish.com/iglibrary/search/MCPB0000757.html
ISBN:	9781627054416

Background subtraction :theory and practice /
Elgammal, Ahmed,

Background subtraction :theory and practice /Ahmed Elgammal - 1 online resource (xvi, 67 pages) :illustrations - Synthesis lectures on computer vision,#62153-1064 ;. - Synthesis lectures on computer vision ;#9..

Includes bibliographical references (pages 55-66)

1. Object detection and segmentation in videos -- 1.1 Characterization of video data -- 1.2 What is foreground and what is background? -- 1.3 The space of solutions -- 1.3.1 Foreground detection vs. background subtraction -- 1.3.2 Video segmentation and motion segmentation -- 1.4 Background subtraction concept

Background subtraction is a widely used concept for detection of moving objects in videos. In the last two decades there has been a lot of development in designing algorithms for background subtraction, as well as wide use of these algorithms in various important applications, such as visual surveillance, sports video analysis, motion capture, etc. Various statistical approaches have been proposed to model scene backgrounds. The concept of background subtraction also has been extended to detect objects from videos captured from moving cameras. This book reviews the concept and practice of background subtraction. We discuss several traditional statistical background subtraction models, including the widely used parametric Gaussian mixture models and non-parametric models. We also discuss the issue of shadow suppression, which is essential for human motion analysis applications. This book discusses approaches and tradeoffs for background maintenance

ISBN: 9781627054416

Standard No.: 10.2200 / S00613ED1V01Y201411COV006doiSubjects--Topical Terms:

433790
Image processing
--Digital techniquesIndex Terms--Genre/Form:

382946
Electronic books

LC Class. No.: TA1655 / .E432 2015

Dewey Class. No.: 621.367

Background subtraction :theory and practice /
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245 1 0 $a Background subtraction : $b theory and practice / $c Ahmed Elgammal
264 1 $a San Rafael, California : $b Morgan & Claypool Publishers, $c [2015]
300 $a 1 online resource (xvi, 67 pages) : $b illustrations
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490 0 $a Synthesis lectures on computer vision, $x 2153-1064 ; $v #6
504 $a Includes bibliographical references (pages 55-66)
505 0 $a 1. Object detection and segmentation in videos -- 1.1 Characterization of video data -- 1.2 What is foreground and what is background? -- 1.3 The space of solutions -- 1.3.1 Foreground detection vs. background subtraction -- 1.3.2 Video segmentation and motion segmentation -- 1.4 Background subtraction concept
505 8 $a 2. Background subtraction from a stationary camera -- 2.1 Introduction -- 2.2 Challenges in scene modeling -- 2.3 Probabilistic background modeling -- 2.4 Parametric background models -- 2.4.1 A single Gaussian background modeL -- 2.4.2 A mixture Gaussian background model -- 2.5 Non-parametric background models -- 2.5.1 Kernel density estimation (KDE) -- 2.5.2 KDE background models -- 2.5.3 KDE-background practice and other non-parametric models -- 2.6 Other background models -- 2.6.1 Predictive-filtering background models -- 2.6.2 Hidden Markov model background subtraction -- 2.6.3 Subspace methods for background subtraction -- 2.6.4 Neural network models -- 2.7 Features for background modeling -- 2.8 Shadow suppression -- 2.8.1 Color spaces and achromatic shadows -- 2.8.2 Algorithmic approaches for shadow detection -- 2.9 Tradeoffs in background maintenance
505 8 $a 3. Background subtraction from a moving camera -- 3.1 Difficulties in the moving-camera case -- 3.2 Motion-compensation -based background-subtraction techniques -- 3.3 Motion segmentation -- 3.4 Layered-motion segmentation -- 3.5 Motion- segmentation-based background-subtraction approaches -- 3.5.1 Orthographic camera, factorization-based background models -- 3.5.2 Dense Bayesian appearance modeling -- 3.5.3 Moving away from the affine assumption, manifold-based background models
505 8 $a Bibliography -- Author's biography
520 3 $a Background subtraction is a widely used concept for detection of moving objects in videos. In the last two decades there has been a lot of development in designing algorithms for background subtraction, as well as wide use of these algorithms in various important applications, such as visual surveillance, sports video analysis, motion capture, etc. Various statistical approaches have been proposed to model scene backgrounds. The concept of background subtraction also has been extended to detect objects from videos captured from moving cameras. This book reviews the concept and practice of background subtraction. We discuss several traditional statistical background subtraction models, including the widely used parametric Gaussian mixture models and non-parametric models. We also discuss the issue of shadow suppression, which is essential for human motion analysis applications. This book discusses approaches and tradeoffs for background maintenance
520 3 $a This book also reviews many of the recent developments in background subtraction paradigm. Recent advances in developing algorithms for background subtraction from moving cameras are described, including motion-compensation-based approaches and motion-segmentation-based approaches
588 $a Online resource; title from PDF title page (Morgan & Claypool, viewed on December 24, 2014)
650 0 $a Image processing $x Digital techniques $3 433790
650 0 $a Image stabilization $3 437869
655 4 $a Electronic books $3 382946
776 0 8 $i Print version: $z 9781627054409
830 0 $a Synthesis lectures on computer vision ; $v #9. $x 2153-1056 $3 437840
856 4 0 $u http://portal.igpublish.com/iglibrary/search/MCPB0000757.html

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