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1、LearningaNon-linearKnowledgeTransferModelforCross-ViewActionRecognitionHosseinRahmani,andAjmalMianComputerScienceandSoftwareEngineering,TheUniversityofWesternAustraliahossein@csse.uwa.edu.au,ajmal.mian@uwa.edu.auAbstractThispaperconcernsactionrecognitionfromunseenandunknownv
2、iews.Weproposeunsupervisedlearningofanon-linearmodelthattransfersknowledgefrommul-tipleviewstoacanonicalview.TheproposedNon-linearKnowledgeTransferModel(NKTM)isadeepnetwork,withweightdecayandsparsityconstraints,whichfindsasharedhigh-levelvirtualpathfromvideoscapturedfromdiffe
3、r-Figure1:Existingcross-viewactionrecognitiontechniques[11,entunknownviewpointstothesamecanonicalview.The12,19,33,40]connectsourceandtargetviewswithasetoflinearstrengthofourtechniqueisthatwelearnasingleNKTMfortransformationsthatareunabletocapturethenon-linearmanifoldsallacti
4、onsandallcameraviewingdirections.Thus,NKTMonwhichrealactionslie.OurNKTMfindsasharedhigh-leveldoesnotrequireactionlabelsduringlearningandknowl-non-linearvirtualpaththatconnectsmultiplesourceandtargetedgeofthecameraviewpointsduringtrainingortesting.viewstothesamecanonicalview.N
5、KTMislearnedonceonlyfromdensetrajectoriesofsyn-theticpointsfittedtomocapdataandthenappliedtorealApracticalsystemshouldbeabletorecognizehumanvideodata.Trajectoriesarecodedwithageneralcode-actionsfromdifferentunknownandmoreimportantlyun-booklearnedfromthesamemocapdata.NKTMissca
6、lableseenviews.Oneapproachforrecognizingactionsacrosstonewactionclassesandtrainingdataasitdoesnotre-viewpointsistocollectdatafromallpossibleviewsandquirere-learning.ExperimentsontheIXMASandN-UCLAtrainaseparateclassifierforeachview.However,thisap-datasetsshowthatNKTMoutperform
7、sexistingstate-of-the-proachdoesnotscalewellasitrequiresalargenumberofartmethodsforcross-viewactionrecognition.labeledsamplesforeachview.Toovercomethisproblem,sometechniquesinfer3Dscenestructureandusegeometrictransformationstoachieveviewinvariance[4,8,23,29,39].Thesemethodso
8、ftenrequirerobustjointestimationwhich1.Introductionisstillanopenprobleminre
