Chaoyi Tan, Xiangtian Li, Xiaobo Wang et al.
Thispaperaimstoresearchandimplementa real-timevideotargettrackingalgorithmbasedon ConvolutionalNeuralNetworks(CNN),enhancingthe accuracyandrobustnessoftargettrackingincomplex scenarios.Addressingthelimitationsoftraditionaltracking algorithmsinhandlingissuessuchastargetocclusion,morphologicalchanges,andbackgroundinterference,our approachintegratestargetdetectionandtrackingstrategies.It continuouslyupdatesthetargetmodelthroughanonline learningmechanismtoadapttochangesinthetarget's appearance.Experimentalresultsdemonstratethat,when dealingwithsituationsinvolvingrapidmotion,partial occlusion,andcomplexbackgrounds,theproposedalgorithm exhibitshighertrackingsuccessratesandlowerfailurerates comparedtoseveralmainstreamtrackingalgorithms.This studysuccessfullyappliesCNNtoreal-timevideotarget tracking,improvingtheaccuracyandstabilityofthetracking algorithmwhilemaintaininghighprocessingspeeds,thus meetingthedemandsofreal-timeapplications.Thisalgorithm isexpectedtoprovidenewsolutionsfortargettrackingtasksin videosurveillanceandintelligenttransportationdomains.