復雜條件下視頻運動目標檢測和跟蹤一、本文概述Overviewofthisarticle隨著計算機視覺技術的飛速發展，視頻運動目標檢測和跟蹤已成為許多領域，如智能監控、人機交互、自動駕駛等的關鍵技術。然而，在實際應用中，視頻序列往往受到多種復雜條件的影響，如光照變化、遮擋、噪聲干擾等，這些因素極大地增加了目標檢測和跟蹤的難度。因此，研究復雜條件下視頻運動目標的有效檢測和跟蹤方法具有重要的理論價值和實際應用意義。Withtherapiddevelopmentofcomputervisiontechnology,videomotiontargetdetectionandtrackinghavebecomekeytechnologiesinmanyfields,suchasintelligentmonitoring,human-computerinteraction,autonomousdriving,etc.However,inpracticalapplications,videosequencesareoftenaffectedbyvariouscomplexconditions,suchaslightingchanges,occlusion,noiseinterference,etc.Thesefactorsgreatlyincreasethedifficultyofobjectdetectionandtracking.Therefore,studyingeffectivedetectionandtrackingmethodsforvideomotiontargetsundercomplexconditionshasimportanttheoreticalvalueandpracticalapplicationsignificance.本文旨在探討和研究在復雜條件下視頻運動目標的檢測和跟蹤技術。文章將對現有的目標檢測和跟蹤算法進行全面的回顧和分析，總結其優缺點和適用場景。在此基礎上，文章將深入探討和研究針對復雜條件的目標檢測和跟蹤算法，包括但不限于基于深度學習的目標檢測算法、抗遮擋和光照變化的跟蹤算法等。Thisarticleaimstoexploreandstudythedetectionandtrackingtechniquesofvideomovingtargetsundercomplexconditions.Thearticlewillcomprehensivelyreviewandanalyzeexistingobjectdetectionandtrackingalgorithms,summarizetheiradvantages,disadvantages,andapplicablescenarios.Onthisbasis,thearticlewilldelveintoandstudyobjectdetectionandtrackingalgorithmsforcomplexconditions,includingbutnotlimitedtodeeplearningbasedobjectdetectionalgorithms,antiocclusionandlightingchangetrackingalgorithms,etc.本文還將通過大量的實驗驗證所提出算法的有效性和魯棒性，并對比和分析不同算法在復雜條件下的性能表現。文章將對未來的研究方向和應用前景進行展望，以期為相關領域的研究人員提供有益的參考和啟示。Thisarticlewillalsoverifytheeffectivenessandrobustnessoftheproposedalgorithmthroughalargenumberofexperiments,andcompareandanalyzetheperformanceofdifferentalgorithmsundercomplexconditions.Thearticlewillprovideprospectsforfutureresearchdirectionsandapplicationprospects,inordertoprovideusefulreferencesandinsightsforresearchersinrelatedfields.二、相關工作Relatedwork在視頻處理和分析領域，運動目標檢測和跟蹤一直是研究的熱點和難點。隨著計算機視覺技術的不斷發展，越來越多的方法被提出并應用于實際場景中。本節將回顧和分析與本文研究內容相關的工作，包括傳統的運動目標檢測與跟蹤方法、深度學習在運動目標檢測與跟蹤中的應用，以及復雜條件下視頻運動目標檢測與跟蹤面臨的挑戰。Inthefieldofvideoprocessingandanalysis,motionobjectdetectionandtrackinghavealwaysbeenahotanddifficultresearchtopic.Withthecontinuousdevelopmentofcomputervisiontechnology,moreandmoremethodsareproposedandappliedinpracticalscenarios.Thissectionwillreviewandanalyzetheworkrelatedtotheresearchcontentofthisarticle,includingtraditionalmotionobjectdetectionandtrackingmethods,theapplicationofdeeplearninginmotionobjectdetectionandtracking,andthechallengesfacedbyvideomotionobjectdetectionandtrackingundercomplexconditions.傳統的運動目標檢測與跟蹤方法主要基于背景建模、光流法、幀間差分等方法。這些方法在簡單背景下能夠取得較好的效果，但在復雜條件下，如光照變化、遮擋、動態背景等，其性能往往受到嚴重影響。為了解決這個問題，研究者們開始嘗試將深度學習技術引入運動目標檢測與跟蹤領域。Thetraditionalmethodsofmotiontargetdetectionandtrackingaremainlybasedonbackgroundmodeling,opticalflowmethod,interframedifference,andothermethods.Thesemethodscanachievegoodresultsinsimplebackgrounds,buttheirperformanceisoftenseverelyaffectedincomplexconditionssuchaslightingchanges,occlusion,dynamicbackgrounds,etc.Toaddressthisissue,researchershavebeguntoattempttointroducedeeplearningtechniquesintothefieldofmotionobjectdetectionandtracking.深度學習，特別是卷積神經網絡（CNN）和循環神經網絡（RNN），為視頻處理和分析提供了新的思路。通過訓練大量的數據，深度學習模型能夠學習到豐富的特征表示和時空上下文信息，從而在復雜條件下實現更準確的運動目標檢測與跟蹤。近年來，基于深度學習的目標檢測算法，如YOLO、SSD、FasterR-CNN等，在速度和精度上都取得了顯著的進展。同時，一些研究者還將深度學習應用于光流估計、背景建模等任務，進一步提高了運動目標檢測與跟蹤的性能。Deeplearning,especiallyConvolutionalNeuralNetworks(CNN)andRecurrentNeuralNetworks(RNN),providesnewideasforvideoprocessingandanalysis.Bytrainingalargeamountofdata,deeplearningmodelscanlearnrichfeaturerepresentationsandspatiotemporalcontextualinformation,therebyachievingmoreaccuratemotiontargetdetectionandtrackingundercomplexconditions.Inrecentyears,deeplearningbasedobjectdetectionalgorithmssuchasYOLO,SSD,FasterR-CNN,etc.havemadesignificantprogressinbothspeedandaccuracy.Meanwhile,someresearchershavealsoapplieddeeplearningtotaskssuchasopticalflowestimationandbackgroundmodeling,furtherimprovingtheperformanceofmotiontargetdetectionandtracking.然而，盡管深度學習在運動目標檢測與跟蹤中取得了顯著的成果，但在復雜條件下仍面臨諸多挑戰。例如，當目標受到嚴重遮擋或光照變化時，深度學習模型可能無法準確提取目標的特征；當場景中存在多個相似目標時，如何實現準確的目標跟蹤也是一個亟待解決的問題。因此，如何在復雜條件下實現魯棒的運動目標檢測與跟蹤仍是當前研究的重點。However,althoughdeeplearninghasachievedsignificantresultsinmotiontargetdetectionandtracking,itstillfacesmanychallengesundercomplexconditions.Forexample,whenthetargetisseverelyoccludedorchangesinlighting,deeplearningmodelsmaynotbeabletoaccuratelyextractthefeaturesofthetarget;Whentherearemultiplesimilartargetsinthescene,achievingaccuratetargettrackingisalsoanurgentproblemtobesolved.Therefore,howtoachieverobustmotiontargetdetectionandtrackingundercomplexconditionsisstillafocusofcurrentresearch.本文旨在研究復雜條件下視頻運動目標檢測與跟蹤的關鍵技術。通過深入分析傳統方法和深度學習方法的優缺點，本文提出了一種基于深度學習的運動目標檢測與跟蹤算法，旨在解決復雜條件下目標檢測與跟蹤面臨的難題。本文還將對所提出算法的性能進行實驗驗證，并與現有方法進行對比分析，以展示其在復雜條件下的優越性和實用性。Thisarticleaimstostudythekeytechnologiesofvideomotionobjectdetectionandtrackingundercomplexconditions.Throughin-depthanalysisoftheadvantagesanddisadvantagesoftraditionalmethodsanddeeplearningmethods,thispaperproposesamotionobjectdetectionandtrackingalgorithmbasedondeeplearning,aimingtosolvethedifficultiesfacedbyobjectdetectionandtrackingundercomplexconditions.Thisarticlewillalsoconductexperimentalverificationontheperformanceoftheproposedalgorithmandcompareitwithexistingmethodstodemonstrateitssuperiorityandpracticalityundercomplexconditions.三、復雜條件下視頻運動目標檢測算法Videomotionobjectdetectionalgorithmundercomplexconditions在復雜條件下進行視頻運動目標檢測是一個具有挑戰性的任務，它涉及到從復雜多變的背景中準確識別并提取出運動目標的信息。這一過程涉及多種算法和技術的結合，包括但不限于背景建模、特征提取、目標分類以及后處理優化等步驟。Videomotiontargetdetectionundercomplexconditionsisachallengingtaskthatinvolvesaccuratelyidentifyingandextractinginformationaboutmotiontargetsfromcomplexandever-changingbackgrounds.Thisprocessinvolvesthecombinationofmultiplealgorithmsandtechnologies,includingbutnotlimitedtobackgroundmodeling,featureextraction,targetclassification,andpost-processingoptimization.背景建模是復雜條件下視頻運動目標檢測的基礎。由于復雜環境可能包含光照變化、動態背景、遮擋等因素，因此背景建模需要具有足夠的魯棒性和自適應性。常見的背景建模方法包括基于統計模型的建模、基于學習的建模以及基于深度學習的建模等。這些方法通過對背景像素進行建模，從而能夠區分出前景目標和背景。Backgroundmodelingisthefoundationofvideomotionobjectdetectionundercomplexconditions.Duetothefactthatcomplexenvironmentsmaycontainfactorssuchaslightingchanges,dynamicbackgrounds,andocclusion,backgroundmodelingneedstohavesufficientrobustnessandadaptability.Commonbackgroundmodelingmethodsincludestatisticalmodel-basedmodeling,learningbasedmodeling,anddeeplearningbasedmodeling.Thesemethodscandistinguishforegroundtargetsfrombackgroundbymodelingbackgroundpixels.特征提取是視頻運動目標檢測的關鍵步驟。在復雜條件下，特征提取需要考慮到光照變化、噪聲干擾以及運動模糊等因素。常用的特征提取方法包括顏色特征、紋理特征、形狀特征以及運動特征等。這些方法可以從視頻幀中提取出有用的信息，為后續的目標分類提供有效的輸入。Featureextractionisacrucialstepinvideomotionobjectdetection.Undercomplexconditions,featureextractionneedstoconsiderfactorssuchaslightingchanges,noiseinterference,andmotionblur.Commonfeatureextractionmethodsincludecolorfeatures,texturefeatures,shapefeatures,andmotionfeatures.Thesemethodscanextractusefulinformationfromvideoframesandprovideeffectiveinputforsubsequenttargetclassification.目標分類是視頻運動目標檢測的核心任務。在復雜條件下，目標分類需要處理類間差異小、類內差異大以及噪聲干擾等問題。為此，可以采用多種分類器進行組合使用，如支持向量機、隨機森林、卷積神經網絡等。這些分類器通過對提取的特征進行學習和分類，從而能夠準確地識別出運動目標。Targetclassificationisthecoretaskofvideomotionobjectdetection.Undercomplexconditions,targetclassificationneedstodealwithissuessuchassmallinterclassdifferences,largeintraclassdifferences,andnoiseinterference.Forthispurpose,multipleclassifierscanbeusedincombination,suchassupportvectormachines,randomforests,convolutionalneuralnetworks,etc.Theseclassifierscanaccuratelyidentifymovingtargetsbylearningandclassifyingtheextractedfeatures.后處理優化是提升視頻運動目標檢測性能的重要手段。在復雜條件下，后處理優化可以進一步消除誤檢和漏檢，提高檢測的準確性和穩定性。常見的后處理優化方法包括形態學處理、幀間融合、軌跡平滑等。這些方法通過對檢測結果進行進一步的處理和優化，從而得到更加準確和可靠的運動目標信息。Postprocessingoptimizationisanimportantmeanstoimprovetheperformanceofvideomotionobjectdetection.Undercomplexconditions,post-processingoptimizationcanfurthereliminatefalsepositivesandmisseddetections,improvingtheaccuracyandstabilityofdetection.Commonpost-processingoptimizationmethodsincludemorphologicalprocessing,interframefusion,trajectorysmoothing,etc.Thesemethodsfurtherprocessandoptimizethedetectionresultstoobtainmoreaccurateandreliablemotiontargetinformation.復雜條件下視頻運動目標檢測算法需要綜合考慮背景建模、特征提取、目標分類以及后處理優化等多個方面。通過不斷優化和改進算法，可以提高視頻運動目標檢測的準確性和魯棒性，為視頻監控、智能交通、人機交互等領域的應用提供有力支持。Undercomplexconditions,videomotionobjectdetectionalgorithmsneedtocomprehensivelyconsidermultipleaspectssuchasbackgroundmodeling,featureextraction,objectclassification,andpost-processingoptimization.Bycontinuouslyoptimizingandimprovingalgorithms,theaccuracyandrobustnessofvideomotiontargetdetectioncanbeimproved,providingstrongsupportforapplicationsinfieldssuchasvideosurveillance,intelligenttransportation,andhuman-computerinteraction.四、復雜條件下視頻運動目標跟蹤算法Videomotiontargettrackingalgorithmundercomplexconditions在復雜條件下，視頻運動目標的跟蹤是一項極具挑戰性的任務。由于光照變化、遮擋、噪聲、動態背景以及攝像機抖動等因素的存在，使得目標跟蹤算法需要具備更強的魯棒性和適應性。為此，本文提出了一種基于深度學習的復雜條件下視頻運動目標跟蹤算法。Trackingvideomovingtargetsundercomplexconditionsisahighlychallengingtask.Duetofactorssuchaschangesinlighting,occlusion,noise,dynamicbackground,andcamerashake,targettrackingalgorithmsneedtohavestrongerrobustnessandadaptability.Therefore,thisarticleproposesavideomotiontargettrackingalgorithmbasedondeeplearningundercomplexconditions.該算法首先利用深度學習模型對視頻幀進行特征提取。具體來說，我們使用卷積神經網絡（CNN）來提取目標的顏色、紋理和形狀等特征信息。然后，結合目標的運動信息，如光流和軌跡，構建一個聯合特征表示，用于描述目標的運動狀態。Thisalgorithmfirstutilizesdeeplearningmodelstoextractfeaturesfromvideoframes.Specifically,weuseConvolutionalNeuralNetworks(CNNs)toextractfeatureinformationsuchascolor,texture,andshapeofthetarget.Then,combiningthemotioninformationofthetarget,suchasopticalflowandtrajectory,ajointfeaturerepresentationisconstructedtodescribethemotionstateofthetarget.在跟蹤過程中，我們采用了一種基于粒子濾波的跟蹤框架。粒子濾波是一種基于概率密度函數估計的序貫蒙特卡洛方法，它通過非參數化的方式逼近任意狀態的后驗概率密度，從而實現對目標狀態的估計。在本文中，我們將粒子濾波與深度學習相結合，利用深度學習提取的特征信息來指導粒子濾波的采樣過程，從而實現對目標的準確跟蹤。Duringthetrackingprocess,weadoptedatrackingframeworkbasedonparticlefiltering.ParticlefilteringisasequentialMonteCarlomethodbasedonprobabilitydensityfunctionestimation,whichapproximatestheposteriorprobabilitydensityofanystateinanonparametricmanner,therebyachievingestimationofthetargetstate.Inthisarticle,wecombineparticlefilteringwithdeeplearningandusethefeatureinformationextractedbydeeplearningtoguidethesamplingprocessofparticlefiltering,therebyachievingaccuratetrackingoftargets.為了應對復雜條件下的挑戰，我們還引入了一種在線學習機制。該機制允許算法在跟蹤過程中不斷學習和更新目標模型，以適應目標外觀的變化。具體來說，我們利用當前幀的目標信息來更新目標模型，以提高算法對目標外觀變化的適應能力。Toaddressthechallengesundercomplexconditions,wehavealsointroducedanonlinelearningmechanism.Thismechanismallowsthealgorithmtocontinuouslylearnandupdatethetargetmodelduringthetrackingprocesstoadapttochangesintheappearanceofthetarget.Specifically,weusethetargetinformationofthecurrentframetoupdatethetargetmodel,inordertoimprovethealgorithm'sadaptabilitytochangesintheappearanceofthetarget.我們還采用了一種多尺度跟蹤策略。由于目標在視頻中的尺度可能會發生變化，因此，我們需要在不同的尺度上對目標進行跟蹤。通過多尺度跟蹤，我們可以更好地適應目標尺度的變化，從而提高算法的跟蹤性能。Wealsoadoptedamulti-scaletrackingstrategy.Duetothepossibilityofchangesinthescaleofthetargetinthevideo,weneedtotrackthetargetatdifferentscales.Throughmulti-scaletracking,wecanbetteradapttochangesinthetargetscale,therebyimprovingthetrackingperformanceofthealgorithm.我們還設計了一種基于運動一致性的遮擋處理方法。當目標被遮擋時，我們可以通過分析目標的運動一致性來檢測遮擋事件的發生，并采取相應的措施來恢復跟蹤。具體來說，我們利用目標的運動信息來構建一個運動模型，并通過比較當前幀與前一幀的運動信息來檢測遮擋事件的發生。當檢測到遮擋事件時，我們會調整粒子的分布以重新找到被遮擋的目標，從而恢復跟蹤。Wealsodesignedanocclusionprocessingmethodbasedonmotionconsistency.Whenthetargetisoccluded,wecandetecttheoccurrenceofocclusioneventsbyanalyzingthemotionconsistencyofthetarget,andtakecorrespondingmeasurestorestoretracking.Specifically,weusethemotioninformationofthetargettoconstructamotionmodelanddetecttheoccurrenceofocclusioneventsbycomparingthemotioninformationofthecurrentframewiththepreviousframe.Whenanocclusioneventisdetected,weadjustthedistributionofparticlestorediscovertheoccludedtargetandrestoretracking.本文提出的復雜條件下視頻運動目標跟蹤算法結合了深度學習、粒子濾波、在線學習和多尺度跟蹤等多種技術，旨在提高算法在復雜環境下的魯棒性和適應性。實驗結果表明，該算法在各種復雜條件下均取得了良好的跟蹤效果，為視頻運動目標跟蹤領域的研究提供了新的思路和方法。Thevideomotiontargettrackingalgorithmproposedinthisarticlecombinesvarioustechnologiessuchasdeeplearning,particlefiltering,onlinelearning,andmulti-scaletrackingundercomplexconditions,aimingtoimprovetherobustnessandadaptabilityofthealgorithmincomplexenvironments.Theexperimentalresultsshowthatthealgorithmhasachievedgoodtrackingperformanceundervariouscomplexconditions,providingnewideasandmethodsforresearchinthefieldofvideomotiontargettracking.五、綜合實驗與性能評估Comprehensiveexperimentsandperformanceevaluation為了驗證本文提出的復雜條件下視頻運動目標檢測和跟蹤算法的有效性，我們進行了一系列綜合實驗，并對算法的性能進行了全面評估。Inordertoverifytheeffectivenessofthevideomotionobjectdetectionandtrackingalgorithmproposedinthisarticleundercomplexconditions,weconductedaseriesofcomprehensiveexperimentsandcomprehensivelyevaluatedtheperformanceofthealgorithm.實驗數據集包含了多種復雜場景，如光照變化、遮擋、攝像頭抖動、背景干擾等。我們采用了公開數據集和自建數據集相結合的方式，以確保實驗的廣泛性和代表性。公開數據集包括PETS2TUD-Brussels和IVC等，自建數據集則模擬了多種實際場景，并進行了人工標注。Theexperimentaldatasetincludesvariouscomplexscenes,suchaslightingchanges,occlusion,camerashake,backgroundinterference,etc.Weadoptedacombinationofpublicandselfbuiltdatasetstoensurethebreadthandrepresentativenessoftheexperiment.ThepublicdatasetincludesPETS2TUDBrusselsandIVC,whiletheselfbuiltdatasetsimulatesvariouspracticalscenariosandismanuallyannotated.為了全面評估算法性能，我們采用了多種評估指標，包括準確率（Precision）、召回率（Recall）、F1分數、平均跟蹤速度（FPS）以及跟蹤成功率（SuccessRate）。這些指標能夠綜合反映算法在不同復雜條件下的表現。Tocomprehensivelyevaluatetheperformanceofthealgorithm,weusedvariousevaluationmetrics,includingaccuracy,recall,F1score,averagetrackingspeed(FPS),andtrackingsuccessrate.Theseindicatorscancomprehensivelyreflecttheperformanceofthealgorithmunderdifferentcomplexconditions.實驗結果顯示，本文提出的算法在復雜條件下表現出了良好的性能。在光照變化、遮擋等場景下，算法的準確率和召回率均保持在較高水平。同時，通過優化算法結構，平均跟蹤速度也得到了顯著提升，滿足了實時性要求。Theexperimentalresultsshowthatthealgorithmproposedinthisarticleexhibitsgoodperformanceundercomplexconditions.Theaccuracyandrecallofthealgorithmremainatahighlevelinscenariossuchaslightingchangesandocclusion.Meanwhile,byoptimizingthealgorithmstructure,theaveragetrackingspeedhasalsobeensignificantlyimproved,meetingthereal-timerequirements.在攝像頭抖動和背景干擾等復雜條件下，本文算法同樣展現出了優秀的性能。通過引入背景建模和抖動補償等策略，算法成功地克服了這些干擾因素，實現了準確的目標跟蹤。Undercomplexconditionssuchascamerashakeandbackgroundinterference,thealgorithmpresentedinthispaperalsodemonstratesexcellentperformance.Byintroducingstrategiessuchasbackgroundmodelingandjittercompensation,thealgorithmsuccessfullyovercomestheseinterferencefactorsandachievesaccuratetargettracking.我們還對算法進行了魯棒性測試。實驗結果表明，本文算法在不同場景下均能保持較高的跟蹤成功率，展現出良好的魯棒性。Wealsoconductedrobustnesstestingonthealgorithm.Theexperimentalresultsshowthatthealgorithmproposedinthispapercanmaintainahightrackingsuccessrateindifferentscenarios,demonstratinggoodrobustness.通過綜合實驗與性能評估，驗證了本文提出的復雜條件下視頻運動目標檢測和跟蹤算法的有效性。該算法在多種復雜場景下均表現出了良好的性能，具有較高的準確率和實時性，為實際應用提供了有力支持。Theeffectivenessoftheproposedvideomotionobjectdetectionandtrackingalgorithmundercomplexconditionshasbeenverifiedthroughcomprehensiveexperimentsandperformanceevaluations.Thisalgorithmhasshowngoodperformanceinvariouscomplexscenarios,withhighaccuracyandreal-timeperformance,providingstrongsupportforpracticalapplications.我們也注意到在某些極端情況下，算法性能仍有提升空間。未來工作將致力于進一步優化算法結構，提高其在復雜條件下的魯棒性和準確性。我們還將研究如何將該算法應用于更多實際場景，如智能交通、安防監控等領域，以推動相關技術的發展。Wealsonoticedthatinsomeextremecases,thereisstillroomforimprovementinalgorithmperformance.Futureworkwillfocusonfurtheroptimizingthealgorithmstructure,improvingitsrobustnessandaccuracyundercomplexconditions.Wewillalsostudyhowtoapplythisalgorithmtomorepracticalscenarios,suchasintelligenttransportation,securitymonitoring,andotherfields,topromotethedevelopmentofrelatedtechnologies.六、結論與展望ConclusionandOutlook本文圍繞“復雜條件下視頻運動目標檢測和跟蹤”這一核心議題進行了深入的理論探討與實證分析。通過對現有算法與技術的系統梳理，結合具體的應用場景，本文揭示了復雜環境下目標檢測與跟蹤所面臨的挑戰與困難，并提出了一系列針對性的解決方案。Thisarticleconductsin-depththeoreticalexplorationandempiricalanalysisaroundthecoretopicof"videomotionobjectdetectionandtrackingundercomplexconditions".Throughasystematicreviewofexistingalgorithmsandtechnologies,combinedwithspecificapplicationscenarios,thisarticlerevealsthechallengesanddifficultiesfacedbyobjectdetectionandtrackingincomplexenvironments,andproposesaseriesoftargetedsolutions.在結論部分，本文的主要工作可概括為以下幾點：詳細分析了復雜環境下目標檢測與跟蹤的主要難點，包括光照變化、遮擋、動態背景等因素對目標特征提取和跟蹤算法性能的影響。基于深度學習的目標檢測算法在復雜環境下表現出了較好的魯棒性和準確性，特別是在處理背景干擾和尺度變化等問題時優勢顯著。再次，針對復雜環境中的目標跟蹤問題，本文探討了多種跟蹤算法的性能表現，并提出了結合多特征融合和在線學習機制的跟蹤策略，有效提高了跟蹤的穩定性和精度。Intheconclusionsection,themainworkofthisarticlecanbesummarizedasfollows:adetailedanalysisofthemaindifficultiesoftargetdetectionandtrackingincomplexenvironments,includingtheimpactoflightingchanges,occlusion,dynamicbackground,andotherfactorsontheperformanceoftargetfeatureextractionandtrackingalgorithms.Theobjectdetectionalgorithmbasedondeeplearninghasshowngoodrobustnessandaccuracyincomplexenvironments,especiallyindealingwithbackgroundinterferenceandscalechanges,withsignificantadvantages.Onceagain,inresponsetotheproblemoftargettrackingincomplexenvironments,thisarticleexplorestheperformanceofvarioustrackingalgorithmsandproposesatrackingstrategythat