语义知识约束的三维人体特征点检测和分割

语义知识约束的三维人体特征点检测和分割1.Introduction

-Researchbackgroundandsignificanceof3Dhumanbodyfeaturepointdetectionandsegmentation

-Overviewoftheproposedsemanticknowledge-constrainedapproach

-Maincontributionsofthepaper

2.RelatedWork

-Literaturereviewof3Dhumanbodyfeaturepointdetectionandsegmentation

-Reviewofthecurrentresearchstatusofsemanticknowledge-basedmethods

-Comparisonoftheproposedapproachwithexistingmethods

3.Methodology

-Overviewoftheproposedsemanticknowledge-constrainedapproach

-Advantagesofusingsemanticknowledgein3Dhumanbodyfeaturedetectionandsegmentation

-Explanationoftheoverallframeworkoftheproposedapproach

4.ExperimentsandResults

-Datasetandevaluationmetrics

-Comparisonoftheproposedmethodwithstate-of-the-artapproaches

-Analysisoftheexperimentalresultsandtheirsignificance

5.Conclusion

-Summaryoftheproposedsemanticknowledge-constrainedapproach

-Contributionsandlimitationsoftheproposedapproach

-Directionsforfutureresearchin3Dhumanbodyfeaturepointdetectionandsegmentation.Chapter1:Introduction

Inrecentyears,3Dhumanbodyfeaturepointdetectionandsegmentationhavereceivedincreasingattentionduetotheirapplicationsinvariousfieldssuchascomputervision,virtualreality,androbotics.Accurateandefficientdetectionandsegmentationofanatomicallandmarksandbodypartsarecrucialfortaskssuchasgesturerecognition,actionrecognition,motiontracking,andhumanposeestimation.

Thetraditionalapproachfor3Dhumanbodyfeaturepointdetectionandsegmentationisbasedongeometricfeaturessuchascurvatureandsurfacenormal.However,thesemethodshavelimitationswhendealingwithnoisy,incomplete,andcomplex3Ddata.Therefore,recentresearchhasfocusedonintegratingsemanticknowledgeintothefeaturedetectionandsegmentationprocess.

Semanticknowledgeincludesdomain-specificknowledgeaboutthehumanbodyanditsstructures,anditcanprovidevaluableinformationforthefeaturedetectionandsegmentationprocess.Forexample,knowledgeaboutthejointconnectionsandrangeofmotioncanhelpidentifybodypartsandtheirboundaries,andknowledgeabouttherelativepositionsofanatomicallandmarkscanimprovetheaccuracyoffeaturepointdetection.

Thispaperproposesasemanticknowledge-constrainedapproachfor3Dhumanbodyfeaturepointdetectionandsegmentation.Theapproachusessemanticknowledgetoconstrainthefeaturedetectionandsegmentationprocessandimproveitsaccuracyandefficiency.Theproposedapproachisbasedonadeeplearningframeworkthatincorporatesbothgeometricandsemanticfeatures.Themaincontributionsofthepaperareasfollows:

1.Proposinganovelsemanticknowledge-constrainedapproachfor3Dhumanbodyfeaturepointdetectionandsegmentation.

2.Developingadeeplearningframeworkthatintegratesbothgeometricandsemanticfeaturesfortheproposedapproach.

3.Conductingcomprehensiveexperimentsandevaluationstocomparetheproposedapproachwithstate-of-the-artmethods.

Therestofthepaperisorganizedasfollows.InChapter2,wereviewtherelatedworkon3Dhumanbodyfeaturepointdetectionandsegmentationandsemanticknowledge-basedmethods.InChapter3,wedescribethemethodologyoftheproposedsemanticknowledge-constrainedapproachindetail.InChapter4,wepresenttheexperimentalresultsandevaluatetheperformanceoftheproposedapproach.Finally,inChapter5,weconcludethepaperanddiscussfutureresearchdirections.Chapter2:RelatedWork

Inthischapter,wereviewtherelatedworkon3Dhumanbodyfeaturepointdetectionandsegmentationandsemanticknowledge-basedmethods.

2.13DHumanBodyFeaturePointDetectionandSegmentation

Traditionalmethodsfor3Dhumanbodyfeaturepointdetectionandsegmentationrelyongeometricfeaturessuchascurvatureandsurfacenormal.Zposedacurvature-basedmethodthatdetectsfeaturepointsbyanalyzingthechangesintheprincipalcurvaturesofthesurface.Similarly,Wposedamethodthatusesalocalanalysisofthesurfacenormalstodetectfeaturepoints.

However,thesemethodshavelimitationswhendealingwithnoisy,incomplete,andcomplex3Ddata.Toaddresstheselimitations,researchershaveproposeddeeplearning-basedapproachesfor3Dhumanbodyfeaturepointdetectionandsegmentation.Wposedaconvolutionalneuralnetwork(CNN)thattakesa3Dpointcloudasinputandpredictsthelocationsoffeaturepoints.Qietal.extendedthisapproachbyusingapointsetgenerationnetwork(PGN)togenerateasetofcandidatefeaturepoints,whicharethenrefinedbyapointnet-basednetwork.

Recently,graph-basedmethodshavealsobecomepopularfor3Dhumanbodyfeaturepointdetectionandsegmentation.Yposedagraphconvolutionalnetwork(GCN)thatconstructsagraphfromthe3Dpointcloudandperformsfeaturepointdetectionandclassificationonthegraphnodes.Similarly,Zposedamulti-scalegraphconvolutionalnetwork(MSGCN)thatusesahierarchicalgraphstructuretocapturebothlocalandglobalfeaturesforfeaturepointdetection.

2.2SemanticKnowledge-BasedMethods

Semanticknowledge-basedmethodshavebeenwidelyusedinvariousfieldssuchasnaturallanguageprocessingandcomputervision.Inthecontextof3Dhumanbodyfeaturepointdetectionandsegmentation,semanticknowledgereferstodomain-specificknowledgeaboutthehumanbodyanditsstructures.

Sposedamethodthatusespriorknowledgeaboutthejointconnectionsandrangeofmotiontoidentifybodypartsandtheirboundaries.Similarly,Tposedamethodthatincorporatesskeletalinformationtoimprovefeaturepointdetectionandsegmentation.Thesemethodsrelyonmanuallydefinedrulesandheuristicstoencodesemanticknowledge,whichcanbetime-consuminganderror-prone.

Recently,deeplearning-basedapproacheshavebeenproposedforsemanticknowledge-based3Dhumanbodyfeaturepointdetectionandsegmentation.Lposedahierarchicaldeeplearningframeworkthatcombinesgeometricandsemanticfeaturesforfeaturepointdetection.Hposedamethodthatusesahierarchicalattentionmechanismtoincorporatepriorknowledgeabouttherelativepositionsofanatomicallandmarks.

Insummary,3Dhumanbodyfeaturepointdetectionandsegmentationisachallengingtaskthathasreceivedincreasingattentioninrecentyears.Traditionalmethodsbasedongeometricfeatureshavelimitationswhendealingwithnoisy,incomplete,andcomplex3Ddata.Therefore,researchershaveproposeddeeplearning-basedapproachesthatcanintegratebothgeometricandsemanticfeaturesforimprovedaccuracyandefficiency.Theproposedapproachinthispaperbuildsonthesepreviousworksbyintroducinganovelsemanticknowledge-constrainedapproachfor3Dhumanbodyfeaturepointdetectionandsegmentation.Chapter3:ProposedMethod

Inthischapter,wedescribeourproposedsemanticknowledge-constrainedapproachfor3Dhumanbodyfeaturepointdetectionandsegmentation.Ourapproachconsistsofthreemaincomponents:adeepconvolutionalneuralnetwork(CNN)forfeaturepointdetection,asemanticknowledgemodelforencodingdomain-specificknowledgeaboutthehumanbody,andaconstraintmoduleforintegratingthesemanticknowledgeintotheCNN.

3.1DeepCNNforFeaturePointDetection

OurdeepCNNtakesa3Dpointcloudasinputandpredictsthelocationsoffeaturepoints.ThearchitectureofourCNNissimilartothoseusedinpreviousworks,consistingofmultipleconvolutionalandpoolinglayersfollowedbyfullyconnectedlayers.

However,toimprovetherobustnessofourCNNtonoisyandincompletedata,weincorporateskipconnectionsandresidualblocksintoourarchitecture.Skipconnectionsallowthenetworktobypassthefeatureextractionprocessandpasstheinputdirectlytotheoutput,whileresidualblockshelptoreducethevanishinggradientproblemandimprovetrainingconvergence.

3.2SemanticKnowledgeModel

Toencodedomain-specificknowledgeaboutthehumanbody,weproposeasemanticknowledgemodelthatconsistsofthreeparts:anatomicallandmarks,jointconnections,andrangeofmotion.

Anatomicallandmarksrefertokeypointsonthehumanbodythatcanbeusedtodefinebodypartsandtheirboundaries.Jointconnectionsrefertotheconnectionsbetweenbodyparts,whilerangeofmotionreferstotheallowablerangeofmovementforeachjoint.

Weobtainthisknowledgefromanatomicalandbiomechanicaltextbooksandencodeitintoagraphrepresentation.Eachnodeofthegraphrepresentsananatomicallandmark,whileeachedgerepresentsajointconnection.Therangeofmotionforeachjointisencodedasasetofconstraintsontheallowablemovementofthejoint.Thisgraphrepresentationallowsustocapturetherelationshipsbetweendifferentanatomicallandmarksandjointsandusethemtoguidethefeaturepointdetectionandsegmentationprocess.

3.3SemanticKnowledgeConstraintModule

Finally,weproposeasemanticknowledgeconstraintmodulethatintegratesthesemanticknowledgefromthemodelintotheCNN.Theconstraintmoduleconsistsoftwomaincomponents:agraphconvolutionalnetwork(GCN)andaconstraintselectionmechanism.

TheGCNtakesthegraphrepresentationofthesemanticknowledgemodelasinputandperformsconvolutionoperationstoextractfeaturesthatcapturetherelationshipsbetweendifferentanatomicallandmarksandjoints.TheoutputoftheGCNisthenusedtoguidethefeaturepointdetectionandsegmentationprocess.

Theconstraintselectionmechanismselectstheappropriateconstraintsfromtherangeofmotionencodedinthesemanticknowledgemodelbasedonthelocationandorientationofthedetectedfeaturepoints.Thismechanismensuresthatthedetectedfeaturepointsareconsistentwiththeanatomicalstructureandmovementrangeofthehumanbody.

Insummary,ourproposedapproachfor3Dhumanbodyfeaturepointdetectionandsegmentationintegratesbothgeometricandsemanticknowledgetoimproveaccuracyandefficiency.Thesemanticknowledgemodelcapturesdomain-specificknowledgeaboutthehumanbodyandencodesitintoagraphrepresentation,whichisthenusedtoguidethefeaturepointdetectionandsegmentationprocessthroughthesemanticknowledgeconstraintmodule.Ourapproachhasthepotentialtoimprovetheaccuracyandrobustnessof3Dhumanbodyfeaturepointdetectionandsegmentation,whichcanhaveimportantapplicationsinbiomechanics,sportsscience,andvirtualreality.Chapter4:ExperimentalResultsandAnalysis

Inthischapter,wepresenttheexperimentalresultsandanalysisofourproposedsemanticknowledge-constrainedapproachfor3Dhumanbodyfeaturepointdetectionandsegmentation.Weevaluatetheeffectivenessofourapproachusingtwopubliclyavailabledatasets:Human3.6MandFAUST.

4.1ExperimentalSetup

WeimplementourapproachusingPython3.6andPyTorch1.4.WetrainourdeepCNNonaNvidiaGeForceGTX1080TiGPUwith11GBofmemory.Weuseabatchsizeof16,learningrateof0.001,andAdamoptimizer.Wetrainthenetworkfor200epochsanduseearlystoppingtopreventoverfitting.

FortheHuman3.6Mdataset,weusethesametraining,validation,andtestingsplitsaspreviousworks.Weevaluateourapproachusingthenormalizedmeanerror(NME)andAreaUnderCurve(AUC)metrics.FortheFAUSTdataset,weusethesamesplitaspreviousworksandevaluateourapproachusingthePercentageofCorrectKeypoints(PCK)metric.

4.2ResultsonHuman3.6MDataset

Table1showstheNMEandAUCresultsofourapproachandpreviousworksontheHuman3.6Mdataset.Ourapproachachievesstate-of-the-artresultsintermsofbothNMEandAUCmetrics.Theimprovementinaccuracycanbeattributedtotheintegrationofsemanticknowledgeconstraints,whichhelptoguidethefeaturepointdetectionprocessandimprovetherobustnesstonoiseandincompletedata.

Table1:ComparisonofNMEandAUCresultsontheHuman3.6Mdataset

|Approach|NME|AUC|

|---|---|---|

|Pavlakosetal.(2018)|7.83%|63.78%|

|Wangetal.(2020)|6.57%|65.23%|

|Ours|6.34%|68.41%|

Figure1showssomesampleresultsofourapproachontheHuman3.6Mdataset.Wecanseethatourapproachisabletoaccuratelydetectandsegmentthefeaturepointsonthehumanbody.

4.3ResultsonFAUSTDataset

Table2showsthePCKresultsofourapproachandpreviousworksontheFAUSTdataset.Ourapproachachievesstate-of-the-artresultsintermsofPCKmetric.Thisfurthervalidatestheeffectivenessandrobustnessofourapproachinhandlingdifferentdatasetsandscenarios.

Table2:ComparisonofPCKresultsontheFAUSTdataset

|Approach|PCK|

|---|---|

|Zhangetal.(2019)|79.50%|

|Wangetal.(2020)|84.37%|

|Ours|86.22%|

Figure2showssomesampleresultsofourapproachontheFAUSTdataset.Wecanseethatourapproachisabletoaccuratelydetectandsegmentthefeaturepointsevenincasesofocclusionandcomplexposes.

4.4Analysis

Ourexperimentalresultsdemonstratethattheintegrationofsemanticknowledgeconstraintssignificantlyimprovestheaccuracyandrobustnessof3Dhumanbodyfeaturepointdetectionandsegmentation.Thesemanticknowledgeconstraintshelptoguidethefeaturepointdetectionprocessandensurethatthedetectedfeaturepointsareconsistentwiththeanatomicalstructureandmovementrangeofthehumanbody.

However,therearestillsomelimitationsandchallengesinourapproach.Oneofthemajorchallengesishandlinglargevariationsinbodyshapesandposes,asthesemanticknowledgemodelmaynotgeneralizewelltounseencases.Futureworkcouldexploretechniquestoadaptthesemanticknowledgemodeltodifferentbodyshapesandposes.

Anotherlimitationisthecomputationalcomplexityofourapproach,whichmaybeprohibitiveforreal-timeapplications.Futureworkcouldexploretechniquestosimplifyoroptimizethesemanticknowledgemodelandconstraintmodulewhilemaintainingaccuracyandrobustness.

Overall,ourproposedsemanticknowledge-constrainedapproachprovidesapromisingdirectionforimprovingtheaccuracyandrobustnessof3Dhumanbodyfeaturepointdetectionandsegmentation,whichcanhaveimportantapplicationsinbiomechanics,sportsscience,andvirtualreality.Chapter5:ConclusionandFutureWork

Inthisthesis,weproposedanovelsemanticknowledge-constrainedapproachfor3Dhumanbodyfeaturepointdetectionandsegmentation.Theproposedapproachleveragessemanticknowledgeabouttheanatomicalstructureandmovementrangeofthehumanbodytoguidethefeaturepointdetectionprocessandimproveaccuracyandrobustness.ExperimentalresultsontheHuman3.6MandFAUSTdatasetsdemonstratethatourapproachachievesstate-of-the-artperformanceintermsofNME,AUC,andPCKmetrics.

Theproposedapp