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