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智能视频监控中多目标检测跟踪技术研究作者：尹晓华学位授予单位：西安电子科技大学参考文献(54条)1.CollinsRAsystemforvideosurveillanceandmonitoring:VSAMfinalreport20002.Humanmotionanalysis:areview.Source1999(03)3.王亮.胡卫明.谭铁牛人运动的视觉分析综述[期刊论文]-计算机学报2002(3)4.汪德洋智能视频监控关键技术研究[学位论文]硕士20055.周兵运动对象检测及其在视频监控中的应用[学位论文]博士20036.HaritaogluI.HarwoodD.DavisLW4:real-timesurveillanceofpeopleandtheiractivities2000(08)7.DeyangWang.WeixinXie.JihongPei.ZongqingLuMovingAreaDetectionBasedonEstimationofStaticBackground2005(01)8.McKennaSTrackinggroupsofpeople2000(01)9.KarmannK.BrandtAMovingobjectrecognitionusinganadaptivebackgroundmemory199010.KilgerMAshadowhandlerinavideo-basedreal-timetrafficmonitoringsystem199211.StaufferC.GrimsonWAdaptivebackgroundmixturemodelsforreal-timetracking199912.LiptonA.FujiyoshiH.PatilRMovingtargetclassificationandtrackingfromreal-timevideo199813.卢宗庆基于梯度场的鲁棒光流估计方法14.ZongqingLuDynamicTextureRecognitionbySpatio-temporalMultiresolutionHistograms15.ZongqingLuARobustOpticalFlowEstimationunderVaryingIllumination2005(04)16.MeyerD.DenzlerJ.NiemannHModelbasedextractionofarticulatedobjectsinimagesequencesforgaitanalysis199717.WildesR.WixsonLDetectingSalientMotionUsingSpatiotemporalFiltersandOpticalFlow199918.WelchG.BishopGAnintroductiontotheKalmanfilter200019.IsardM.BlakeACondensation-conditionaldensitypropagationforvisualtracking1998(01)20.PavlovicV.RehgJ.ChamT-J.MurphyKAdynamicBayesiannetworkapproachtofiguretrackingusinglearneddynamicmodels199921.JuS.BlackM.YaccobYCardboardpeople:aparameterizedmodelofarticulatedimagemotion199622.KaraulovaI.HallP.MarshallAAhierarchicalmodelofdynamicsfortrackingpeoplewithasinglevideocamera200023.NiyogiS.AdelsonEAnalyzingandrecognizingwalkingfiguresinXYT199424.RohrKTowardsmodel-basedrecognitionofhumanmovementsinimagesequences1994(01)25.WrenC.AzarbayejaniA.DarrellT.PentlandAPfinder:real-timetrackingofthehumanbody1997(07)26.ParagiosN.DericheRGeodesicactivecontoursandlevelsetsforthedetectionandtrackingofmovingobjects2000(03)27.PeterfreundNRobusttrackingofpositionandvelocitywithKalmansnakes2000(06)28.PolanaR.NelsonRLowlevelrecognitionofhumanmotion199429.SegsnJ.PingaliSAcamera-basedsystemfortrackingpeopleinrealtime199630.JangD-S.ChoiH-IActivemodelsfortrackingmovingobjects2000(07)31.UtsumiA.MoriH.OhyaJ.YachidaMMultiple-view-basedtrackingofmultiplehumans199832.CaiQ.AggarwalJTrackinghumanmotionusingmultiplecameras199633.RCucchiara.CGrana.MPiccardi.A.PratiDetectingobjects,sahdowsandghostsinvideostreamsbyexploitingcolorandmotioninformation200134.CucchiaraR.GranaC.PiccardiM.Prati,AImprovingShadowSuppressioninMovingObjectDetectionwithHSVColorInformation200135.AElgammal.DHarwood.LSDavisNon-parametricmodelforbackgroundsubtraction199936.NHerodotou.KNPlataniotis.ANVenetsanopoulosAcolorsegmentationschemeforobject-basedvideocoding199837.THorprasert.DHarwood.LSDavisAstatisticalapproachforreal-timerobustbackgroundsubtractionandshadowdetection199938.BobickAFTheKidsRoom:Aperceptually-BasedInteractiveandImmersiveStoryEnviroments1999(04)39.FieguthP.DTerzopoulosColor-BasedTrackingofHeadsandOtherMobileObjectsatVideoFrameRates199740.GrimsonWELUsingApadativeTrackingtoClassifyandMonitorActivitiesinaSite199841.SKhan.MShahTrackingpeopleinpresenceofocclusion200042.RoberttCollins.Alanjlipton.hironobufujitoshi.TakeokanadeAlgorithmForcooperativeMultisentersurveillance2001(10)43.SectionI,videosurveillanceandmonitoring199844.MBogaert.NChleq.PCornez.C.S.Regazzoni,A.Teschioni,M.ThonnatThepasswordsproject199645.MRota.MThonnatVideosequenceinterpretationforvisualsurveillance200046.TheViewsprojectandwide-areasurveillance199247.HBuxton.SGGongVisualsurveillanceinadynamicanduncertainworld199548.TMatsuyamaCooperativedistributedvision199849.PHKelly.AKatkere.DYKuramura.S.Moezzi,S.ChatterjeeR.JainAnArchitectureforMultiplePerpectiveInteractiveVideo199550.LLee.RRomano.GSteinMonitoringActivitiesfromMultipleVideoStreams:EstablishingaCommonCoordinateFrame2000(08)51.潘征基于视觉的多目标检测与跟踪技术[学位论文]硕士200352.SKhan.OJaved.MShahTrackinginUncalibratedCameraswithOverlappingFieldofView200153.VeraKettnaker.RaminZabihBayesianMulti-CameraSurveillance199954.HannaPasula.StuartRussell.MichaelOstland.Ya'acovRitovTrackingManyObjectswithManySensors1999相似文献(10条)1.学位论文刘德健运动人体目标的多视角匹配与跟踪方法研究2007智能视频监控可以智能地监控目标行为，检测非正常事件，有着重要的理论意义和应用价值。实用的监控系统，必须能够适应不同的场景、不同的光照条件，能够很好的处理室内、室外目标的检测、跟踪问题。本文围绕多摄像头运动人体目标的检测跟踪技术展开研究，内容包括运动目标投影阴影的消除、光滑地面的目标倒影的消除、基于相似性度量的多摄像机目标关联方法和双摄像头目标的协同跟踪问题。针对传统的固定参数的阴影检测方法的不足，提出一种自适应的阴影特性学习方法．针对人体目标在光滑地面造成的倒影，提出一种基于人体模型的倒影消除方法；针对多摄像头之间的目标传递问题，提出一种目标榴似性度量方法，该方法通过将人体目标等分切割，提取目标特征矢量，最后使用贴近度最大最小法计算两个目标的相似性；针对双摄像头监控场景，跟踪过程中应用单应矩阵将两个视场中目标坐标转换到顶视图中分析比较，同时使用本文提出的目标相似性度量方法，从空间位置和目标相似性两个方面对目标进行匹配关联，从而达到良好协同跟踪的目的。2.学位论文陈勇智能视频监控中运动目标检测技术研究2007计算机智能视频监控是计算机视觉领域一个新兴的应用方向和备受关注的前言课题，是计算机科学、机器视觉、图像工程、模式识别、人工智能等多学科高科技的结晶。运动物体视觉分析作为智能监控中的一项核心技术，它包括运动物体检测与提取、物体分类、事件检测、行为识别和分析等，而运动物体检测与提取又是其中的基础和关键。在运动物体检测与提取中，阴影的存在会导致物体的错误分类或者使不同物体相互融合，为后续的高级处理带来错误的结果，导致不能够很好的跟踪物体以及对物体的行为进行理解和描述。本文在总结和分析了国内外相关研究的基础上，分别在前景区域检测与提取、运动物体的阴影检测与去除及目标跟踪技术三个方面作了一定的研究：1)在前景区域检测与提取中，讨论各种常用的背景提取方法原理时，着重分析了混合高斯背景建模方法，指出减背景技术中存在分割阈值难以合理选取的不足。选用混合