Background Subtraction: Theory and Practice (Synthesis Lectures on Computer Vision, Band 6)

85 Pages·2014·5.835 MB·English

by  Ahmed Elgammal

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Background Subtraction
eoryandPractice

Synthesis Lectures on
Computer Vision
Editor
GérardMedioni,UniversityofSouthernCalifornia
SvenDickinson,UniversityofToronto
SynthesisLecturesonComputerVisioniseditedbyGérardMedionioftheUniversityofSouthern
CaliforniaandSvenDickinsonoftheUniversityofToronto.eseriespublishes50-to150page
publicationsontopicspertainingtocomputervisionandpatternrecognition.
BackgroundSubtraction:eoryandPractice
AhmedElgammal
2014
Vision-BasedInteraction
MatthewTurkandGangHua
2013
CameraNetworks:eAcquisitionandAnalysisofVideosoverWideAreas
AmitK.Roy-ChowdhuryandBiSong
2012
DeformableSurface3DReconstructionfromMonocularImages
MathieuSalzmannandPascalFua
2010
Boosting-BasedFaceDetectionandAdaptation
ChaZhangandZhengyouZhang
2010
Image-BasedModelingofPlantsandTrees
SingBingKangandLongQuan
2009
Copyright©2015byMorgan&Claypool
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin
anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations
inprintedreviews,withoutthepriorpermissionofthepublisher.
BackgroundSubtraction:eoryandPractice
AhmedElgammal
www.morganclaypool.com
ISBN:9781627054409 paperback
ISBN:9781627054416 ebook
DOI10.2200/S00613ED1V01Y201411COV006
APublicationintheMorgan&ClaypoolPublishersseries
SYNTHESISLECTURESONCOMPUTERVISION
Lecture#6
SeriesEditors:GérardMedioni,UniversityofSouthernCalifornia
SvenDickinson,UniversityofToronto
SeriesISSN
Print2153-1056 Electronic2153-1064
Background Subtraction
eoryandPractice
Ahmed Elgammal
RutgersUniversity
SYNTHESISLECTURESONCOMPUTERVISION#6
M
&C Morgan &cLaypool publishers
ABSTRACT
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 back-
groundsubtraction,aswellaswideuseofthesealgorithmsinvariousimportantapplications,such
as visual surveillance, sports video analysis, motion capture, etc. Various statistical approaches
have been proposed to model scene backgrounds. e concept of background subtraction also
has been extended to detect objects from videos captured from moving cameras. is book re-
viewstheconceptandpracticeofbackgroundsubtraction.Wediscussseveraltraditionalstatistical
backgroundsubtractionmodels,includingthewidelyusedparametricGaussianmixturemodels
andnon-parametricmodels.Wealsodiscusstheissueofshadowsuppression,whichisessential
for humanmotionanalysisapplications.isbookdiscussesapproaches andtradeoffsfor back-
groundmaintenance.isbookalsoreviewsmanyoftherecentdevelopmentsinbackgroundsub-
traction paradigm. Recent advances in developing algorithms for background subtraction from
moving cameras are described, including motion-compensation-based approaches and motion-
segmentation-basedapproaches.
Forlinkstothevideostoaccompanythisbook,pleasesee
https://sites.google.com/a/morganclaypool.com/backgroundsubtraction/.
KEYWORDS
background subtraction, segmentation, visual surveillance, gaussian mixure model,
kerneldensityestimation,movingobjectdetection,shadowdetection,figure-ground
segmentation, motion segmentation, motion compensation, layered scene segmen-
tation
vii
To my wife for her emotional support,
to my parents for all what they have given me,
and to my kids for the joy they bring to my life.
– Ahmed