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1、3DMean-ShiftTrackingofHumanBodyPartsandRecognitionofWorkingActionsinanIndustrialEnvironmentMarkusHahn1,FuadQuronfuleh1,ChristianW¨ohler2,andFranzKummert31DaimlerAG,GroupResearchandAdvancedEngineering,Ulm,Germany2ImageAnalysisGroup,DortmundUniversityofTechnology,Dortmund,Germany3Applied
2、Informatics,BielefeldUniversity,Bielefeld,GermanyAbstract.Inthisstudywedescribeamethodfor3Dtrajectorybasedrecognitionofanddiscriminationbetweendifferentworkingactionsinanindustrialenvironment.Amotion-attributed3Dpointcloudrepresentsthescenebasedonimagesofasmall-baselinetrinocularcameras
3、ystem.Atwo-stagemean-shiftalgorithmisusedfordetectionand3Dtrack-ingofallmovingobjectsinthescene.Asequenceofworkingactionsisrecognisedwithaparticlefilterbasedmatchingofanon-stationaryHiddenMarkovModel,relyingonspatialcontextandaclassificationoftheobserved3Dtrajectories.Thesystemisabletoex
4、tractanobjectperformingaknownactionoutofamultitudeoftrackedobjects.The3Dtrackingstageisevaluatedwithrespecttoitsmetricaccuracybasedonninereal-worldtestimagesequencesforwhichgroundtruthdataweredetermined.Anexperimentalevaluationoftheactionrecognitionstageisconductedusing20real-worldtest
5、sequencesacquiredfromdif-ferentviewpointsinanindustrialworkingenvironment.Weshowthatoursystemisabletoperform3Dtrackingofhumanbodypartsandasubsequentrecognitionofworkingactionsunderdifficult,realisticcon-ditions.Itdetectsinterruptionsofthesequenceofworkingactionsbyenteringasafetymodeandre
6、turnstotheregularmodeassoonastheworkingactionscontinue.1IntroductionTheefficiencyofmanyindustrialproductionprocessescanbeincreasedbyes-tablishingaclosecollaborationbetweenhumansandmachinesexploitingtheiruniquecapabilities.Asafeinteractionbetweenhumansandindustrialrobotsrequiresvisionmeth
7、odsfor3Dposeestimation,tracking,andrecognitionofthemotionofhumanbodyparts.Arobustmethodoftenusedforobjecttrackingisthemean-shiftalgorithm[2,3,5],whichsearchesforalocalmodeoftheempiricaldensityfunction.Themean-shifttrackingapproachintroducedin[4]isextendedto3Dspacein[12].Intheexperime
