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1、StepwiseMetricPromotionforUnsupervisedVideoPersonRe-identificationZimoLiu1DongWang2∗HuchuanLu3DalianUniversityofTechnology12∗3lzm920316@gmail.comwdice,lhchuan@dlut.edu.cnAbstractTheintensiveannotationcostandtherichbutunlabeledID001datacontainedinvideosmotivateus
2、toproposeanunsuper-visedvideo-basedpersonre-identification(re-ID)method.ID002Westartfromtwoassumptions:1)differentvideotrack-letstypicallycontaindifferentpersons,giventhatthetrack-letsaretakenatdistinctplacesorwithlongintervals;2)withineachtracklet,theframesarem
3、ostlyofthesameper-son.Basedontheseassumptions,thispaperproposeastep-wisemetricpromotionapproachtoestimatetheidentitiesoftrainingtracklets,whichiteratesbetweencross-cameratrackletassociationandfeaturelearning.Specifically,WeFigure1.AvideoframeontheMARSdataset[48]
4、.Wedrawuseeachtrainingtrackletasaquery,andperformretrievalinthisframetwotracklets(redandyellow)whichareactuallyinthecross-cameratrainingset.Ourmethodisbuiltonobservedatdistincttimestamps.Itisintuitivetoassumethatthereciprocalnearestneighborsearchandcaneliminate
5、thetwotrackletscanbetreatedasdifferentidentities.Thisobservationhardnegativelabelmatches,i.e.,thecross-cameranearestcontributestothemodelinitializationprocess.neighborsofthefalsematchesintheinitialranklist.Thetrackletthatpassesthereciprocalnearestneighborchecko
6、ver,sincevideoscontainmorepriorknowledgeabouttheisconsideredtohavethesameIDwiththequery.Exper-scenario,theinfluenceofbackgroundnoisecanbelargelyimentalresultsonthePRID2011,ILIDS-VID,andMARSweakened[42].datasetsshowthattheproposedmethodachievesverycom-Second,vide
7、otracklets,producedbypedestriandetec-petitivere-IDaccuracycomparedwithitssupervisedcoun-tionandtracking(Fig.1),arereliabledatasourceforunsu-terparts.pervisedlearningmethods.Thisprocessisfullyautomaticandunsupervised.Asimpliedintherecentsurvey[50],differenttrack
8、letscanbetreatedasdifferentidentities,as1.IntroductionlongasweassumethatthetrackletsarecapturedatdistinctPersonre-identification(re-ID),aimingtoretrieveaplacesorwithlonginter
