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基于改进CNN框架的人体动作识别.docx

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基于改进CNN框架的人体动作识别 Title:HumanActionRecognitionusingImprovedConvolutionalNeuralNetworkFramework Abstract: Humanactionrecognitionisafundamentaltaskincomputervisionwithvariouspracticalapplications.ConvolutionalNeuralNetworks(CNNs)haveshownremarkableperformanceinimageandvideoclassificationtasks,includinghumanactionrecognition.However,thecomplexityanddiversityofhumanactionsposechallengesforachievingaccurateandrobustrecognition. ThispaperproposesanimprovedCNNframeworkforhumanactionrecognition.TheframeworkaimstoaddresssomeofthelimitationsinexistingCNNarchitectures,suchascapturingspatialandtemporalinformation,handlinglargevariationsinactionappearance,andincorporatingcontextualinformation. 1.Introduction: Humanactionrecognitionhasreceivedsignificantattentionduetoitswiderangeofapplicationsinvideosurveillance,sportsanalysis,healthcare,andhuman-computerinteraction.Traditionalmethodsoftenrelyonhandcraftedfeaturesandrequireextensivedomainknowledge.However,withtheadventofdeeplearning,CNNshavedemonstratedpromisingresultsinseveralcomputervisiontasks,includinghumanactionrecognition. 2.RelatedWork: ThissectionreviewsthepreviousworksonCNN-basedhumanactionrecognitionframeworks.Ithighlightstheadvancementsmadeinspatialandtemporalmodeling,featureextraction,andfusionstrategies.ItalsodiscussesthechallengesfacedbyexistingapproachesandthemotivationfortheproposedimprovedCNNframework. 3.ProposedImprovedCNNFramework: Theproposedframeworkconsistsofseveralkeycomponentsdesignedtoenhancetheperformanceofhumanactionrecognition.Thesecomponentsinclude: 3.1Spatial-temporalConvolutionalLayers: Tocaptureboththespatialandtemporalinformation,theproposedframeworkincorporates3Dconvolutionallayers.Theselayerscombinetheadvantagesof2Dspatialconvolutionalfiltersand1Dtemporalconvolutionfilters,enablingthenetworktolearnbothappearanceandmotioncuessimultaneously. 3.2AttentionMechanism: Toaddressthechallengesoflargevariationsinactionappearanceandachievebetterdiscrimination,anattentionmechanismisintegratedintotheframework