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1、ContextualBoostforPedestrianDetectionYuanyuanDingandJingXiaoEpsonResearchandDevelopment,Inc.214DevconDrive,SanJose,CA95112{yding,xiaoj}@erd.epson.comAbstract[6,17,28,10,45,44,31,36,18,1].Todeterminewhetheralocalwindowboundingahumanfigure,bothgenera-Pedestriandetectionfromima
2、gesisanimportantandyettiveanddiscriminativeapproacheshavebeendevelopedchallengingtask.Theconventionalmethodsusuallyidentify[23].Thegenerativeapproachesinfertheposteriorprob-humanfiguresusingimagefeaturesinsidethelocalregions.abilityforpedestrianclassusingdiscrete[22]orcontin
3、uousInthispaperwepresentthat,besidesthelocalfeatures,[5,13,49]shapemodels,orcombiningshapeandtexturecontextcuesintheneighborhoodprovideimportantcon-models[26,14].Thediscriminativeapproachesextractim-straintsthatarenotyetwellutilized.Weproposeaframe-agefeaturesinthelocalwind
4、owandconstructclassifiersworktoincorporatethecontextconstraintsfordetection.fordetection.ForthispurposevariousfeatureshavebeenFirst,wecombinethelocalwindowwithneighborhoodwin-proposed,suchasHaarwaveletfeatures[42],gradientbaseddowstoconstructamulti-scaleimagecontextdescripto
5、r,features[6],shapebasedfeatures[34],combinationofmul-designedtorepresentthecontextualcuesinspatial,scaling,tiplefeatures[44,45],automaticallyminedfeatures[10],andcolorspaces.Second,wedevelopaniterativeclassifi-orpose-invariantfeatures[27].Thelocalfeaturesarethencationalgori
6、thmcalledcontextualboost.Ateachiteration,usedtoidentifypedestriansintheclassificationprocessbytheclassifierresponsesfromthepreviousiterationacrossalgorithmssuchasAdaBoost[20].Intheliteraturesthistheneighborhoodandmultipleimagescales,calledclas-processhasbeeneithertargetedatth
7、ehumanfigureasonesificationcontext,areincorporatedasadditionalfeaturesobjectorbasedonpartdetectors.Thepart-basedmethodstolearnanewclassifier.Thenumberofiterationsisde-[15,47,19]treatthehumanfigureasanassemblyofdif-terminedinthetrainingprocesswhentheerrorratecon-ferentbodyparts.
8、Theyrundetectiononindividualpartsverges.Sincetheclassificationcontextincorporatesco