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动态背景下基于改进视觉背景提取的前景检测 Title:ForegroundDetectionbasedonImprovedVisualBackgroundExtractionunderDynamicBackground Abstract: Foregrounddetectionisacrucialtaskincomputervisionapplicationssuchassurveillancesystems,objecttracking,andsceneunderstanding.Itaimstoaccuratelyidentifyandextractthemovingobjectsofinterestfromthebackground.However,indynamicbackgrounds,wherethesceneundergoescontinuouschanges,traditionalbackgroundextractionmethodsoftenfailtosegmenttheforegroundaccurately.Toaddressthisissue,thispaperproposesanimprovedvisualbackgroundextractionapproachforrobustforegrounddetectionunderdynamicbackgroundscenarios. 1.Introduction: Foregrounddetectionplaysavitalroleinvariouscomputervisionapplications,includingmotionanalysis,securitysystems,andvideosurveillance.Itinvolvesseparatingtheforeground,whichrepresentstheobjectsofinterest,fromthebackground.Traditionalbackgroundextractionmethodsrelyontheassumptionofstaticbackgrounds,whicharenotsuitablefordynamicscenesthatexhibitconsistentchanges.Thispaperproposesanenhancedapproachforvisualbackgroundextractiontoovercometheselimitationsandimprovetheaccuracyofforegrounddetectionindynamicbackgroundscenarios. 2.RelatedWorks: Acomprehensivereviewofexistingmethodsforforegrounddetectionindynamicbackgroundsispresented.Thissectioncoverstraditionaltechniquessuchaspixel-basedmethods,backgroundsubtraction,andGaussianmixturemodels.Additionally,ithighlightsthelimitationsoftheseapproachesinhandlingdynamicbackgroundsandmotivatestheneedforanadvancedvisualbackgroundextractiontechnique. 3.ProposedMethodology: Theproposedmethodologyforforegrounddetectionunderdynamicbackgroundsisbasedonanimprovedvisualbackgroundextractionapproach.Thekeystepsinvolvedinthemethodologyinclude: 3.1Dynamicbackgroundmodeling: Thisstepfocusesonmodelingthedynamicchangesinthebackgroundusingspatio-temporalinformation.Itconsidersvariousfactorssuchasilluminationchanges,cameramotion,andenvironmentalvariationstocreateanaccuratebackgroundmodelthatadaptstothescenedynamics. 3.2Backgroundlearn