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1、Optik124(2013)5301–5306ContentslistsavailableatScienceDirectOptikjournalhomepage:www.elsevier.de/ijleoAnovelapproachforcrowdvideomonitoringofsubwayplatformsXuemina,∗bbbHu,HongZheng,WenweiWang,XiLiaSchoolofPhysicsandElectronics,HubeiUniversity,368Youy
2、iAvenue,Wuhan,Hubei430062,ChinabLabforImageProcessingandIntelligentSystems,SchoolofElectronicInformation,WuhanUniversity,129LuoyunRoad,Wuhan,Hubei430072,ChinaarticleinfoabstractArticlehistory:Crowdmonitoringinadensecrowdscenehasbecomeanimportantandch
3、allengingtopicinthefieldofReceived16October2012videosurveillancesystem.ThispaperproposesanovelcrowdmonitoringapproachforsubwayplatformsAccepted18March2013toaddressrequirementsinrailtrafficmanagement.Firstly,animprovementforMixtureGaussianback-groundmod
4、elingispresentedtosegmentthecrowd.Intheprocessoffeatureextraction,theconceptoftheweightedareaisproposedtosolvetheproblemoftheperspectiveofimages.TodealwiththeissueKeywords:oftheocclusionbetweenindividuals,animprovedgradientfeatureisdevelopedinthispap
5、er.Andthen,SubwayplatformAdaptiveBoostclassifierwiththefeatureweightedareaandtheimprovedgradientisusedtoestimateWeightedareathegradientcrowddensity.Finally,thecrowdiscountedbythemethodoflinearregression.TheexperimentalImprovedresultsshowthatthepropose
6、dapproachisfeasibleandeffectiveforcrowdmonitoringinrealsubwayDensityestimationplatforms.Crowdcounting©2013ElsevierGmbH.Allrightsreserved.1.Introductionispoorforanexcessivelydensecrowdbecauseoftheocclusionbetweenindividuals.Withtheaccelerationofurbani
7、zation,moreandmorepeoplegoAnotherisbasedonastatisticalgorithmwithglobalorlocaltocitiesforlivelihood,placingtremendouspressureonthecities’features,suchastextures,gradient,area,edgefeaturepoints.Chowinfrastructures.Denselycrowdedplaces,suchassubwaystat
8、ions,andChow[7],Maranaetal.[8]andWuetal.[9]didsomeresearchesareusuallyverycongested,whichhaveanegativeeffectonthepeo-incrowddensityestimation.Ryanetal.[10]andChanetal.[11]pro-pleontheway.Moreover,disastersarepronetohappeninginsuchposedacrowdcountinga