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基于CNN的粒子滤波目标跟踪算法研究.docx

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基于CNN的粒子滤波目标跟踪算法研究 Abstract: Particlefilterisapopularalgorithminobjecttrackingduetoitsabilitytohandlenonlinearandnon-Gaussiansystems.However,thetraditionalparticlefiltersuffersfromhighcomputationalcomplexityandlowtrackingaccuracy.Inthispaper,weproposeanovelparticlefilteralgorithmbasedonconvolutionalneuralnetwork(CNN)forobjecttracking.ByincorporatingCNNintotheparticlefilterframework,ouralgorithmachievesimprovedtrackingperformancewhilereducingcomputationalcomplexity.Experimentalresultsdemonstratetheeffectivenessandefficiencyofourproposedalgorithm. 1.Introduction Objecttrackingisafundamentalproblemincomputervisionandhaswideapplicationsinvariousfieldssuchassurveillance,robotics,andaugmentedreality.Particlefilterisaneffectivealgorithmforobjecttracking,asitcanhandlenonlinearity,non-Gaussiannoise,andocclusioninthetrackingprocess.However,thetraditionalparticlefiltersuffersfromlimitationssuchashighcomputationalcomplexityandlowtrackingaccuracy.Inrecentyears,deeplearningalgorithms,especiallyconvolutionalneuralnetwork(CNN),haveachievedremarkableresultsinvariouscomputervisiontasks. 2.Background 2.1ParticleFilter Particlefilter,alsoknownasMonteCarlofilter,isasequentialMonteCarlomethodforestimatingtheposteriordistributionofthestatevariablesinasystem.Itinvolvesrepresentingtheposteriordistributionwithasetofsamples,knownasparticles,whicharepropagatedandupdatedthroughaseriesofobservations.Theparticleswithhigherweightsaremorelikelytorepresentthetruestateofthesystem. 2.2ConvolutionalNeuralNetwork Convolutionalneuralnetwork(CNN)isatypeofdeeplearningalgorithmthathasshownexcellentperformanceinimageclassification,objectdetection,andsemanticsegmentation.CNNhasahierarchicalstructurewithmultipleconvolutionallayersthatlearnlocalfeaturesfromtheinputdata.ThishierarchicalfeatureextractionabilitymakesCNNaneffectivetoolforimage-basedtasks. 3.ProposedMethod OurproposedparticlefilteralgorithmbasedonCNNaimstoimprovethetrackingperformancewhilereducingcomputationalcomplexity.Themainideaistoutilizethepowe