基于改进高斯混合模型的运动物体的图像检测.docx

基于改进高斯混合模型的运动物体的图像检测
摘要：
本文提出了一种改进高斯混合模型（GMM）的运动物体图像检测方法。传统的GMM方法在处理目标跟踪时容易受到光照条件、背景复杂度等因素的影响，造成误检和漏检现象。本文通过增加背景图像的反比权值、引入颜色模型等方法，对GMM进行改进，增强了对运动物体的检测效果。实验结果表明，该方法在复杂背景下有较强的适应性和鲁棒性，能够有效地检测出运动物体。
关键词：运动物体检测；高斯混合模型；背景建模
Abstract:
Inthispaper,animprovedGaussianMixtureModel(GMM)formotionobjectimagedetectionisproposed.TraditionalGMMmethodsareofteninfluencedbylightingconditions,backgroundcomplexityandotherfactorswhendealingwithtargettracking,leadingtofalsepositivesandfalsenegatives.ThispaperimprovesGMMbyincreasingtheinverseweightofthebackgroundimageandintroducingcolormodels,enhancingthedetectioneffectonmovingobjects.Experimentalresultsshowthatthemethodhasstrongadaptabilityandrobustnessincomplexbackgrounds,andcaneffectivelydetectmovingobjects.
Keywords:Motionobjectdetection;GaussianMixtureModel;backgroundmodeling
Introduction:
Objectdetectionhasalwaysbeenahotresearchtopicinthefieldofcomputervision.Asanimportantcomponentofobjectdetection,motionobjectdetectionplaysanimportantroleinavarietyofapplications,suchasvideosurveillance,trafficmonitoring,andhuman-computerinteraction.Withthedevelopmentofcomputerhardwareandalgorithms,theaccuracyofmotionobjectdetectionhasbeengreatlyimproved.However,traditionalmethodsstillfacechallengessuchasilluminationchanges,complexbackgrounds,andocclusion.Therefore,improvingtheaccuracyandrobustnessofmotionobjectdetectionisanimportantresearchdirection.
Gaussianmixturemodel(GMM)isawidelyusedmethodinmotionobjectdetection.ItmodelsthebackgroundbyamixtureofGaussiandistributions,andthensubtractsthebackgroundmodelfromthecurrentframetoobtaintheforegroundmask.Thebackgroundmodelisupdateddynamicallytoadapttochangesintheenvironment.However,traditionalGMMmethodsareofteninfluencedbyilluminationchanges,backgroundcomplexityandotherfactors,leadingtofalsepositivesandfalsenegatives.
Inthispaper,animprovedGMMmethodisproposedtoenhancethedetectioneffectonmovingobjects.Themethodisbasedontwoaspects:improvingbackgroundmodelingandintroducingcolormodels.Inthebackgroundmodelingas