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步态骨骼模型的协同表示识别方法 Title:CollaborativeRepresentation-BasedRecognitionMethodforGaitSkeletonModel Abstract: Gaitrecognitionhasgainedsignificantattentioninrecentyearsduetoitsnon-intrusivenatureandpotentialapplicationsinsurveillance,humanidentification,andhealthcare.Inthispaper,weproposeaCollaborativeRepresentation-BasedRecognitionMethodforGaitSkeletonModel.Themethodutilizesthespatio-temporalinformationofthegaitskeletonmodeltocapturethedistinctivefeaturesforrecognition.Experimentalresultsonvariousdatasetsdemonstratetheeffectivenessandefficiencyoftheproposedmethod. 1.Introduction: Gaitrecognitionreferstotheprocessofidentifyinganindividualbasedontheirwalkingpatterns.Itisabiometricmodalitythatcanbebothnon-intrusiveanddistance-based.Gaitrecognitionhasgainedincreasingpopularityduetoitswiderangeofapplicationsinsurveillance,securitysystems,andhealthcaremonitoring.Inrecentyears,theuseofdeeplearningtechniqueshassignificantlyimprovedtheaccuracyofgaitrecognition.However,deeplearningmodelsoftenrequirelargeamountsoflabeledtrainingdataandarecomputationallyexpensive.Hence,thereisaneedforamoreefficientandeffectiveapproachtogaitrecognition. 2.RelatedWork: Thissectionbrieflyreviewstheexistingapproachestogaitrecognition,includingmodel-basedmethods,appearance-basedmethods,anddeeplearning-basedmethods.Italsohighlightsthelimitationsoftheseapproaches,suchastherelianceonspecificimagingsensors,theneedforextensivetrainingdata,andcomputationalcomplexity. 3.ProposedMethod: TheCollaborativeRepresentation-BasedRecognitionMethodforGaitSkeletonModelaimstoleveragetheadvantagesofgaitskeletonmodelswhileaddressingthelimitationsofexistingapproaches.Theproposedmethodconsistsofthefollowingsteps: 3.1SkeletonExtraction: Inthisstep,thegaitsequencesarecapturedusingdepthsensorsormotioncapturesystemstoobtaintheskeletalrepresentation.Theskeletonsarerepresentedasasequenceof3Djointcoordinates. 3.2Spatio-TemporalFeatureExtraction: Foreachframeofthegaitsequence,spatio-temporalfeaturesareextractedfromtheskeletonrepresentat