Abnormal Crowd Behavior Detection Based on the Energy Model(9)

(a)Typicalscreen-capturesofthevideo

clip

(b)

curve

(c) curve

Fig.9.

Gathering rstlyandthen

running.

(a)Typicalscreen-capturesofthevideo

clip

(b)

curve(c) curve

Fig.10.

Gatheringandrunningoccuratthesametime.

behaviorinlowandmediumcrowddensitywithlowcom-putationload.Moreaccurateestimationofthecrowddensityandcrowddistributionare rstlyintroducedintothedetectionofabnormalactivities.

However,theshortcomingofthethreshold-basedapproachisobvious:theappropriateestimationofthresholdiscrucialandsensitivetotheeffectiveness.Thethresholdalwayshavetobedeterminedbyexperience.Inthefuturework,wewillcombinethethreshold-basedmethodsandmachine-learning-basedmethodstogethertodetectmorekindsofabnormalbehaviors,suchasbelongingdropping,loitering,crossingoverthefence.Maybesomeappropriatemodelingapproachesandhardwareswithhighcomputationalpowersuchasgraphicsprocessingunit(GPU)willbeincludedin

500

ourfuturework.

REFERENCES

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