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基于粒子群优化的核匹配追踪目标识别(英文).docx

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基于粒子群优化的核匹配追踪目标识别(英文) ParticleSwarmOptimization-BasedNuclearMatchingforTargetIdentificationinMotionTracking Introduction Withtheadvancementintechnology,motiontrackinghasbecomeagrowingconcerninmanyfields,includingsurveillance,militaryoperations,andautonomousvehicles.Motiontrackingaimsatdetectingandidentifyingobjects,andanalyzingtheirmotionpatterns.However,targetidentificationremainsachallengingtask,particularlywhentheobjectsaremovingfastortravelinginacrowdedenvironment.Inthispaper,weproposeanovelapproachtotargetidentificationinmotiontrackingbasedonParticleSwarmOptimization(PSO)andNuclearMatching. Background PSOisapopulation-basedoptimizationtechniquethatisinspiredbytheswarmingbehaviorofbirdsorfish.InPSO,agroupofparticlesmoveswithinasearchspace,witheachparticlerepresentingapotentialsolution.Theparticlesareattractedtothebestsolutiontheyhaveencountered,knownastheglobalbest,andlearnfromtheirindividualbestsolution,knownasthepersonalbest.Theparticles'velocityisupdatedbasedonthesetwobestsolutionsandtheircurrentposition.Theprocessisrepeateduntilthesolutionconvergestotheoptimalornear-optimalsolution. NuclearMatching,ontheotherhand,isamethodusedtomatchpointsintwodifferentimagesbasedontheirnuclearnormdistance.Itisaneffectivemethodforobjectrecognitioninimageswithsignificantilluminationandgeometricchanges.Thenuclearnormdistanceisdefinedasthesumofthesingularvaluesofamatrix,whichcapturestheglobalstructureofthematrix.NuclearMatchingcanbeappliedtomatchingandidentifyingtargetsinmotiontracking,asthemovementoftheobjectcanchangetheappearanceoftheobject,leadingtoilluminationandgeometricchanges. ProposedApproach Inthispaper,weproposeaPSO-basedNuclearMatchingapproachtotargetidentificationinmotiontracking.Ourapproachcomprisesofthefollowingsteps: Step1:ImagePreprocessing Thefirststepinourapproachistopreprocesstheinputimagetoreducenoiseandenhancetheimagequality.Varioustechniquescanbeusedforimagepreprocessing,includingmedianfiltering,Gaussianfiltering,andedgedetection. Step2:FeatureExtraction T