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基于改进的空间直方图相似性度量的粒子滤波视觉跟踪（英文）.docx

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基于改进的空间直方图相似性度量的粒子滤波视觉跟踪（英文） Introduction: Visualtrackingreferstotheprocessofcontinuouslyestimatingthelocationofatargetinanimageorvideosequenceovertime.Ithasextensiveapplicationsincomputervision,includingsurveillance,autonomoussystems,andobjectrecognition.However,visualtrackingisachallengingtaskduetothevariabilityofobjectsinappearance,illumination,occlusion,andmotion.Toovercomethesechallenges,manyalgorithmshavebeenproposedbasedonvarioustechniques,suchastemplatematching,feature-basedmethods,anddeeplearning. Amongthesealgorithms,theparticlefilterisapopularapproachforvisualtrackingduetoitsabilitytohandlenon-linearandnon-Gaussiandistributions.However,theperformanceofparticlefilter-basedvisualtrackingstronglydependsonthesimilaritymeasurebetweenthetargetandtemplate.Therearevarioussimilaritymeasuresusedinparticlefiltertracking,includingcorrelation-basedmethods,histogram-basedmethods,andkernel-basedmethods. Inthispaper,weproposeanimprovedhistogram-basedsimilaritymeasureforparticlefiltervisualtracking.Theproposedmethodenhancesthestandardhistogram-basedsimilaritymeasurebyincorporatingspatialinformationandadaptivebinningtechniques.Theperformanceoftheproposedmethodisevaluatedandcomparedwithstate-of-the-artmethodsonseveralbenchmarkdatasets,demonstratingitseffectivenessinimprovingtheaccuracyandrobustnessofparticlefiltervisualtracking. RelatedWork: Histogram-basedmethodshavebeenusedwidelyinparticlefiltertrackingduetotheirsimplicityandefficiency.Thestandardhistogramsimilaritymeasurecalculatesthedistancebetweenthehistogramofthetargetandtemplatewithoutconsideringspatialinformation.Thisapproachcanbeeffectiveincaseswheretheobjecthasadistinctivecolorortextureandisnotsubjecttochangesinilluminationorpose.However,inmorechallengingscenarios,wheretheobjectundergoessignificantvariationsinappearanceorissubjecttosevereocclusions,thestandardhistogram-basedmethodmayfail. Toimprovetherobustnessofhistogram-basedmethods,researchershaveproposedseveralmodifications.Oneapproachistousecolorhistogramsind