PerceptuallyConsistentExample‐BasedHumanMo9onRetrieval

ZhigangDeng,QinGu,QingLiUniversityofHouston

PresentedBy:DanielDeSousa

GoalsandObjec9ves

•  Givenalargemo9ondatarepositoryandaquerymo9onsequence,retrievesimilarmo9onsequences

•  Ranktheminperceptuallyconsistentorder

•  Performthesearchefficiently

MethodOverview

1  Subdividethehumanbodyintoameaningfulhierarchicalstructure

2  Segmenteachpartandpar99onoriginalsequencesinpart‐basedsequences,thennormalizeframelength

3  Extractmo9onpaSernsbydetec9ngandgroupingsimilarsegments

4  Segmentquerymo9onsequenceinpaSernlists

5  RunmodifiedKMP‐matchingalgorithmtoretrieveandrankmatches

HierarchicalPar99on

Representa9onBenefits

•  Hierarchyrepresenta9on– Controlgranularitytodescribevariouslevels– Mul9‐layerdescribescorrela9onamongvariousparts

•  Jointanglesvs.MarkerPosi9ons– Anglesdescribemo9onregardlessofboneorlimbsizes

Mo9onSegmenta9on

•  Longsequencesofmo9ondatawithlargevaria9oninmovement

•  Automa9callydividesegmentsintomoremeaningfulsegments

•  Automatedmethodformo9onsegmenta9onfoundinBarbicetall.[2004]

Adap9veSegmenta9onProcess

•  Segmentifdimensionalityofmo9onincreasessignificantly

•  Computeprobabilityforeachpointdependingonitsfitinthecurrentaccepteddistribu9on

•  Ateachpeakfollowingalocalminimum,newsegmentiflargerthanathreshold,R

•  Rvalueshigherforupperlevelsofhierarchy,duetomoredegreesoffreedom(performanceandstorageboost)

SegmentNormaliza9on

•  Foreachbodypartinthehierarchy,sequencesaresegmented

•  Producesvaryingnumberofframesforthesame9merangeofmo9on

•  Normalizesallsequencestohavesamenumberofframes,usingCubicSplineinterpola9ontocreatenewframes

Mo9onPaSernExtrac9on

•  Amo9onpaSernisarepresenta9vemo9onsegmentforhierarchicalbodypart

•  OncepaSernsareextractedtheycanbematchedandcompared

•  Adap9veK‐meansclusteringalgorithmgroupssimilarsegmentsintopaSerns

Adap9veK‐meansclustering

•  Ini9alnumberofclusters(K)•  Applythistothemo9onsegmentsfrompart2

•  Iferrormetricislargerthenlimit,increasenumberofclustersandrepeat

•  BeSerresultswithmaximumclusteringerrorvs.averageclusteringerror(buttherewasaperformancepenalty)

UnderlyingDataStructures

•  Theresultoftheseopera9onsproducethreedatastructures:– Kd‐treetostoremo9onpaSerns– Mo9onPaSernIndexLists•  18listsofpaSernindexes,oneforeachbodypart

– PaSernDissimilarityMap•  Matrixcomputedforallmo9onpaSernsbasedonEuclideandistance

Mo9onRetrievalAlgorithm

PaSernSimilarityComparison

•  GivenapaSernindexlist,compu9ngsimilarityisastringmatchingproblemwithsomecaveats– Notafixednumberofcharacters

– Cannotexpecttofindperfectmatches,waytoomanymo9onpaSerns

– Mustfactorin“quasi‐”matches

Compu9ngSimilarity

•  ModifiedtheKMPstringmatchingalgorithm•  IftwopaSernindicessimilarityarewithinthresholdMaxTolerance,thenit’saquasi‐match

•  Ifnumberofconsecu9vequasi‐matchesisgreaterthanMinConNum,scoreisincreased

•  Ifnoquasi‐match,reducethescore

•  Matchingscorenormalizedbasedonlength

SimilarityPropaga9on

•  Wepropagatethesimilarityscoresupward

•  Severaladvantages

•  Method:Sprfl=½xSrf+½xSrlSurfl=αxSrfl+(1‐α)xSprfl

ResultsandComparison

UserStudy

•  Askuserstoratemo9onretrievalssimilarityfrom1to10

Correla9onBetweenUserandNumericalAnalysis

Conclusion

•  Par9cularlygoodatiden9fyingcomplexmo9on

•  Matchesorbeatscurrentmethodsinnumericalandperceptualra9ngs

•  However,cannotspecifytherootpathmo9on– Lenvs.rightmovement

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