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1、ADETECTORTREEOFBOOSTEDCLASSIFIERSFORREAL-TIMEOBJECTDETECTIONANDTRACKINGRainerLienhart,LuhongLiang,andAlexanderKuranovMicrocomputerResearchLabs,IntelCorporationSantaClara,CA,95052{rainer.lienhart,lu.hong.liang,alexander.kuranov}@intel.comABSTRACTTwochalle
2、ngingproblems,however,remainandwillbeThispaperpresentsanoveltreeclassifierforcomplexobjectaddressedbyournoveltreeclassifier:(1)Itisempiricalanddetectiontaskstogetherwithageneralframeworkforreal-timedifficultworktodeterminetherightobjectsub-patternclasses
3、objecttrackinginvideosusingthenoveltreeclassifier.Ainmostcases.Forexample,intuitivelytheopennessandtheboostedtrainingalgorithmwithaclustering-and-splittingstepisappearance(with/withoutfacialhair)aretwoprimaryfactorsofemployedtoconstructbranchesinthenodes
4、recursively,ifandin-classvariabilityofmouthpatterns(seeFig.5).However,inonlyifitimprovesthediscriminativepowercomparedtoapracticeitisoftendifficulttogroupindividualpatternsintothesinglemonolithicnodeclassifierandhasalowercomputationalrightsub-patternclas
5、sduetoambiguitysuchamouthwithacomplexity.Amouthtrackingsystemthatintegratesthetreeshaved,butstillvisiblebeard.(2)MultiplespecializedclassifierundertheproposedframeworkisbuiltandtestedonclassifiersincreasethecomputationalcomplexityconflictingXM2FDBdatabas
6、e.Experimentalresultsshowthatthewiththereal-timerequirementintheappliedobjectdetectiondetectionaccuracyisequalorbetterthanasingleormultipleandtrackingsystem.cascadeclassifier,whilebeingcomputationallessdemanding.Contribution:Firstly,anoveldetectortreeofb
7、oostedclassifiersisintroducedconsideringboththecharacteristicsof1.INTRODUCTIONthepatternsinfeaturespaceandthecomputationalefficiencyinObjectdetectionandtrackinginvideosequenceshavebeenordertoaddressthetwoaforementionedproblems.Ateachintensivelyresearched
8、inrecentyearsduetotheirimportanceinnodeinthetreeaclustering-and-splittingstepisembeddedintoapplicationssuchascontent-basedretrieval,naturalhumanthetrainingalgorithmtoconstructbranchesintheclassifiercomputerinterfaces,objec
