Abnormal Crowd Behavior Detection Based on the Energy Model教案范文英语

AbnormalCrowdBehaviorDetectionBasedontheEnergyModel

ProceedingoftheIEEE

InternationalConferenceonInformationandAutomationShenzhen,ChinaJune2023

AbnormalCrowdBehaviorDetection

BasedontheEnergyModel

GuogangXiong,XinyuWu,Yen-LunChen,andYongshengOu

InstitutesofAdvancedTechnologyChineseAcademyofSciencesShenzhen,GuangdongProvince,China

TheChineseUniversityofHongkong,HongKong,China

{gg.xiong,xy.wu,yl.chen,ys.ou}@

Abstract—Inthispaper,wepresentanovelmethodtodetecttwotypicalabnormalactivities:pedestraingatheringandrunning.Themethodisbasedonthepotentialenergyandkineticenergy.Reliableestimationofcrowddensityandcrowddistributionarerstlyintroducedintothedetectionofanomalies.Estimationofcrowddensityisobtainedfromtheimagepotentialenergymodel.Bybuildingtheforegroundhistogramontheandaxisrespectively,theprobabilitydistributionofthehistogramcanbeobtained,andthenwedenetheCrowdDistributionIndex()torepresentthedispersion.TheCrowdDistributionIndex()isusedtodetectpedestrainsgathering.ThekineticenergyisdeterminedbycomputationofopticalowandCrowdDistributionIndex,andthenusedtodetectpeoplerunning.Thedetectionforabnormalactivitiesisbasedonthethresholdanalysis.Withouttrainingdata,themodelcanrobustlydetectabnormalbehaviorsinlowandmediumcrowddensitywithlowcomputationload.IndexTerms—Intelligentsurveillance,Imagepotentialenergymodel,Abnormalevents,Crowdanalysis.

Shenzhen

objects,suchasbelongingdropping,loiteringandcrossingoverthefence.Asonlyafewpeoplemovinginthescenes,theseapproachescanimplementdetectingandsegmentingeasily.However,whentheenvironmentbecomescompli-cated,asshowninFig.1,thesemethodswillbesubjectedtosevereocclusionswhichmakesthetracking,detectingandsegmentingdifculttoimplement.Basedontheabovefactors,therearefewattemptstomodellargergroupsofpeoplewhichshouldbepaidmoreattention

to.

I.INTRODUCTION

Thedecreasingcostsofvideosurveillanceequipmentshaveresultedinlargevolumesofvideodata.However,thisexcessiveamountofinformationhasnotbeenmetwithenoughhumanoperators[1].Ontheotherhand,techniquesonimageandvideoanalysisdeveloprapidly.Duetotheabovetwofactors,crowdanalysisincomputervisionhasbecomeapopularresearchtopicinnumerouscountries.Modelsabletodetectabnormaleventswithinvideostreamscanservearangeofapplications,suchassecurityautomationsysteminpublic,coalminesurveillanceandintelligentanalysisapplication.Inanysuchcase,automaticalanomalydetectionwouldsignicantlyimprovetheefciencyofvideoanalysis,savingvaluablehumanattentionforonlythemostsalientcontent[2].

Mosttraditionalapproachesonanomalydetectionalwaysaimatspecicanomaliesofsinglepersonorafewmoving

workdescribedinthispaperispartiallysupportedbytheNature

ScienceFoundationofChina(61005012),byShenzhen/HongkongInnova-tionCircleProject(ZYB202307070024A)andbythegrantfromShenzhenpublicscienceandtechnology.TheauthorswouldliketothankMr.RuiqingFu,Mr.LeiZhang,Mr.KeXu,andMr.LongHanfortheirvaluablecontributiontothisproject.

This

(a)People

gathering

Fig.1.

(b)Peoplerunning

Typicalabnormalscenes.

Thispaperaimstopresentaneffectivemodeltodetecttwokindsofanomalieswhicharethemostprimaryandprevalentinpublicscenes.Generallyspeaking,pedestriangatheringandrunningisanemergencysignalindicatingsomeabnormaleventshappening,surveillancesystemsshoulddetectthemautomaticallyintime.Therestofthispaperisorganizedasfollows.AsummaryoftherelatedworkisgiveninSection2.OursystemdiagramisdescribedinSection3.Wepresenttheimagepotentialenergymodeltoestimatethecrowddensityinsection4.InSection5,wedenetheCrowdDistributionIndex.ModieddenitionofkineticenergyisgiveninSection6.InSection7,wepresenttheexperimentalresultsondifferentvideoclips.Inthelastsection,wesummarizetheapproachandpresentsomecluesforfutureresearchwork.

II.RELATED