Abnormal Crowd Behavior Detection Based on the Energy Model(8)

旧人不归 分享 2021-06-02 下载文档

The rsttwovideoclipsareobtainedbyastationarycamerainstalledonthebuildingBofShenzhenInstituteofAdvancedTechnologyinatopdownview.Fig.7andFig.8showtheresultandanalysis.Theoriginalvideoframesizeis320*240.Underthiscircumstance,thethresholdof is6andthethresholdof is

180.

(a)Typicalscreen-capturesofthevideo

clip

(b) curveFig.7.

Pedestrainsgathering.

Inthe rstclipshowninFig.7,peoplearegatheringfromdifferentdirectionsandthenwalktogetherinonedirection,

499

ouralgorithmcorrectlydetectstheanomalyintime.Fig.7(b)showsthecurveof ,theabnormalgatheringoccursatthe218thframe.Asthereisnopeoplerunninginthescene,curveof is

ignored.

(a)Typicalscreen-capturesofthevideo

clip

(b) curve

Fig.8.

Pedestrainsrunning.

InthesecondclipshowninFig.8,peoplewalkinalowspeed,andthenbegintorunsuddenly.Theabnormalrunningisdetectedatthe500thframe.Fig.8(b)showsthecurveof .Nopeoplegatheringinthisclip,sothecurveof isabandoned.InFig.8(b),wecanseeasharppeakwhichisactuallynoiseoccursataboutthe400thframe;however,itonlylast6frames,oursystemavoidsfalsealarmsuccessfully.Anothertwovideoclipsdownloadedfromtheinternetarewithhighcrowddensity,andtwotypicalanomaliesoccurinthesameclip.Theframesizeis768*576.Underthiscircumstance,thethresholdof is12andthethresholdof is40000.Fig.9andFig.10showtheexperimentalresults.

InthethirdclipshowninFig.9,wecanseethecrowddensityishighandocclusionissevere.However,ouralgo-rithmdetectthepedestraingatheringinalocalregionat100thframeeffectively.InFig.9(a), isred,whichindicatesthatpeoplegatheringistakingplace.Fig.9(b)presentthecurveof .Inthesameclip,aftergathering,peoplestarttorunsuddenly.Fig.9(c)showsthecurveof detectedat355thframe.

,whererunningisAsshowninFig.10,atframe44th,ouralgorithmdetectabnormalactivitiestakingplace,while and red,whichmeanspedestrainsgatheringandrunning arebothoccursimultaneously.

VIII.CONCLUSIONS

AND

FUTUREWORK

Inthispaper,wepresentanovelmethodtodetecttwotypicalabnormalactivities:pedestraingatheringandrunning.Withouttrainingdata,themodelcanrobustlydetectabnormal


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