外文翻译运动图像和运动矢量检测综述.docx

外文翻译运动图像和运动矢量检测综述.docx

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外文翻译运动图像和运动矢量检测综述

外文文献：

ASURVEYONMOTIONIMAGE

ANDTHESEARCHOFMOTIONVECTOR

Aftermotiondetection,surveillancesystemsgenerallytrackmovingobjectsfromoneframetoanotherinanimagesequence.Thetrackingalgorithmsusuallyhaveconsiderableintersectionwithmotiondetectionduringprocessing.Trackingovertimetypicallyinvolvesmatchingobjectsinconsecutiveframesusingfeaturessuchaspoints,linesorblobs.UsefulmathematicaltoolsfortrackingincludetheKalmanfilter,theCondensationalgorithm,thedynamicBayesiannetwork,thegeodesicmethod,etc.Trackingmethodsaredividedintofourmajorcategories:

region-basedtracking,active-contour-basedtracking,featurebasedtracking,andmodel-basedtracking.Itshouldbepointedoutthatthisclassificationisnotabsoluteinthatalgorithmsfromdifferentcategoriescanbeintegratedtogether.

A.Region-BasedTracking

Region-basedtrackingalgorithmstrackobjectsaccordingtovariationsoftheimageregionscorrespondingtothemovingobjects.Forthesealgorithms,thebackgroundimageismaintaineddynamically,andmotionregionsareusuallydetectedbysubtractingthebackgroundfromthecurrentimage.Wrenetal.exploretheuseofsmallblobfeaturestotrackasinglehumaninanindoorenvironment.Intheirwork,ahumanbodyisconsideredasacombinationofsomeblobsrespectivelyrepresentingvariousbodypartssuchashead,torsoandthefourlimbs.Meanwhile,bothhumanbodyandbackgroundscenearemodeledwithGaussiandistributionsofpixelvalues.Finally,thepixelsbelongingtothehumanbodyareassignedtothedifferentbodypart’sblobsusingthelog-likelihoodmeasure.Therefore,bytrackingeachsmallblob,themovinghumanissuccessfullytracked.Recently,McKennaetal.[11]proposeanadaptivebackgroundsubtractionmethodinwhichcolorandgradientinformationarecombinedtocopewithshadowsandunreliablecolorcuesinmotionsegmentation.Trackingisthenperformedatthreelevelsofabstraction:

regions,people,andgroups.Eachregionhasaboundingboxandregionscanmergeandsplit.Ahumaniscomposedofoneormoreregionsgroupedtogetherundertheconditionofgeometricstructureconstraintsonthehumanbody,andahumangroupconsistsofoneormorepeoplegroupedtogether.Therefore,usingtheregiontrackerandtheindividualcolorappearancemodel,perfecttrackingofmultiplepeopleisachieved,evenduringocclusion.Asfarasregion-basedvehicletrackingisconcerned,therearesometypicalsystemssuchastheCMSmobilizedsystemsupportedbytheFederalHighwayAdministration（FHWA）,attheJetPropulsion

Laboratory（JPL）,andthePATHsystemdevelopedbytheBerkeleygroup.

Althoughtheyworkwellinscenescontainingonlyafewobjects（suchashighways）,region-basedtrackingalgorithmscannotreliablyhandleocclusionbetweenobjects.Furthermore,asthesealgorithmsonlyobtainthetrackingresultsattheregionlevelandareessentiallyproceduresformotiondetection,theoutlineor3-Dposeofobjectscannotbeacquired.（The3-Dposeofanobjectconsistsofthepositionandorientationoftheobject）.Accordingly,thesealgorithmscannotsatisfytherequirementforsurveillanceagainstaclutteredbackgroundorwithmultiplemovingobjects.

B.ActiveContour-BasedTracking

Activecontour-basedtrackingalgorithmstrackobjectsbyrepresentingtheiroutlinesasboundingcontoursandupdatingthesecontoursdynamicallyinsuccessiveframes.Thesealgorithmsaimatdirectlyextractingshapesofsubjectsandprovidemoreeffectivedescriptionsofobjectsthanregion-basedalgorithms.Paragiosetal.detectandtrackmultiplemovingobjectsinimagesequencesusingageodesicactivecontourobjectivefunctionandalevelsetformulationscheme.PeterfreundexploresanewactivecontourmodelbasedonaKalmanfilterfortrackingnonrigidmovingtargetssuchaspeopleinspatio-velocityspace.Isardetal.adoptstochasticdifferentialequationstodescribecomplexmotionmodels,andcombinethisapproachwithdeformabletemplatestocopewithpeopletracking.Maliketal.havesuccessfullyappliedactivecontour-basedmethodstovehicletracking.Incontrasttoregion-basedtrackingalgorithms,activecontour-basedalgorithmsdescribeobjectsmoresimplyandmoreeffectivelyandreducecomputationalcomplexity.Evenunderdisturbanceorpartialocclusion,thesealgorithmsmaytrackobjectscontinuously.However,thetrackingprecisionislimitedatthecontourlevel.Therecoveryofthe3-Dposeofanobjectfromitscontourontheimageplaneisademandingproblem.Afurtherdifficultyisthattheactivecontour-basedalgorithmsarehighlysensitivetotheinitializationoftracking,makingitdifficulttostarttrackingautomatically.

C.Feature-BasedTracking

Feature-basedtrackingalgorithmsperformrecognitionandtrackingofobjectsbyextractingelements,clusteringthemintohigherlevelfeaturesandthenmatchingthefeaturesbetweenimages.Feature-basedtrackingalgorithmscanfurtherbeclassifiedintothreesubcategoriesaccordingtothenatureofselectedfeatures:

globalfeature-basedalgorithms,localfeature-basedalgorithms,anddependence-graph-basedalgorithms.

•Thefeaturesusedinglobalfeature-basedalgorithmsincludecentroids,perimeters,areas,someordersofquadraturesandcolors,etc.Polanaetal.provideagoodexampleofglobalfeature-basedtracking.Apersonisboundedwitharectangularboxwhosecentroidisselectedasthefeaturefortracking.Evenwhenocclusionhappensbetweentwopersonsduringtracking,aslongasthevelocityofthecentroidscanbedistinguishedeffectively,trackingisstillsuccessful.

•Thefeaturesusedinlocalfeature-basedalgorithmsincludelinesegments,curvesegments,andcornervertices,etc.

•Thefeaturesusedindependence-graph-basedalgorithmsincludeavarietyofdistancesandgeometricrelationsbetweenfeatures.

Theabovethreemethodscanbecombined.IntherecentworkofJangetal.[34],anactivetemplatethatcharacterizesregionalandstructuralfeaturesofanobjectisbuiltdynamicallybasedontheinformationofshape,texture,color,andedgefeaturesoftheregion.UsingmotionestimationbasedonaKalmanfilter,thetrackingofanonrigidmovingobjectissuccessfullyperformedbyminimizingafeatureenergyfunctionduringthematchingprocess.

Ingeneral,astheyoperateon2-Dimageplanes,feature-basedtrackingalgorithmscanadaptsuccessfullyandrapidlytoallowreal-timeprocessingandtrackingofmultipleobjectswhicharerequiredinheavythruwayscenes,etc.However,dependence-graph-basedalgorithmscannotbeusedinreal-timetrackingbecausetheyneedtime-consumingsearchingandmatchingofgraphs.Feature-basedtrackingalgorithmscanhandlepartialocclusionbyusinginformationonobjectmotion,localfeaturesanddependencegraphs.However,thereareseveralseriousdeficienciesinfeature-basedtrackingalgorithms.

•Therecognitionrateofobjectsbasedon2-Dimagefeaturesislow,becauseofthenonlineardistortionduringperspectiveprojectionandtheimagevariationswiththeviewpoint’smovement.

•Thesealgorithmsaregenerallyunabletorecover3-Dposeofobjects.

•Thestabilityofdealingeffectivelywithocclusion,overlappingandinterferenceofunrelatedstructuresisgenerallypoor.

D.Model-BasedTracking

Model-basedtrackingalgorithmstrackobjectsbymatchingprojectedobjectmodels,producedwithpriorknowledge,toimagedata.Themodelsareusuallyconstructedoff-linewithmanualmeasurement,CADtoolsorcomputervisiontechniques.Asmodel-basedrigidobjecttrackingandmodel-basednorigidobjecttrackingarequitedifferent,wereviewseparatelymodel-basedhumanbodytracking（norigidobjecttracking）andmodel-basedvehicletracking（rigidobjecttracking）.

1.Model-BasedHumanBodyTracking:

Thegeneralapproachformodel-basedhumanbodytracingisknownasanalysis-by-synthesis,anditisusedinapredict-match-updatestyle.Firstly,theposeofthemodelforthenextframeispredictedaccordingtopriorknowledgeandtrackinghistory.Then,thepredictedmodelissynthesizedandprojectedintotheimageplaneforcomparisonwiththeimagedata.Aspecificposeevaluationfunctionisneededtomeasurethesimilaritybetweentheprojectedmodelandtheimagedata.Accordingtodifferentsearchstrategies,thisisdoneeitherrecursivelyorusingsamplingtechniquesuntilthecorrectposeisfinallyfoundandisusedtoupdatethemodel.Poseestimationinthefirstframeneedstobehandledspecially.Generally,model-basedhumanbodytrackinginvolvesthreemainissues:

•Constructionofhumanbodymodels；

•Representationofpriorknowledgeofmotionmodelsandmotionconstraints；

•Predictionandsearchstrategies.Previousworkonthesethreeissuesisbrieflyandrespectivelyreviewedasfollows.

A．Humanbodymodels:

Constructionofhumanbodymodelsisthebaseofmodel-basedhumanbodytracking.Generally,themorecomplexahumanbodymodel,themoreaccuratethetrackingresults,butthemoreexpensivethecomputation.Traditionally,thegeometricstructureofhumanbodycanberepresentedinthefollowingfourstyles.

•Stickfigure.Theessenceofhumanmotionistypicallycontainedinthemovementsofthetorso,theheadandthefourlimbs,sothestick-figuremethodistorepresentthepartsofahumanbodyassticksandlinkthestickswithjoints.Karaulovaetal.useastickfigurerepresentationtobuildanovelhierarchicalmodelofhumandynamicsencodedusinghiddenMarkovmodels（HMMs）,andrealizeview-independenttrackingofahumanbodyinmonocularimagesequences.

•2-Dcontour.Thiskindofhumanbodymodelisdirectlyrelevanttohumanbodyprojectionsinanimageplane