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1、Deliverable11.4REPORTONPROGRESSWITHRESPECTTOPARTIALSOLUTIONSONHUMANDETECTIONALGORITHMS,HUMANACTIVITYANALYSISMETHODS,ANDMULTIMEDIADATABASESPreparedbyA.EnisCetinBilkentUniversityAnkara,TurkeySummary:Thisreportconsistsoftwoparts.Thefirstpartoftheprojectdescribesthepart
2、ialsolutionsdevelopedbythemembersofWP-11onhumanactivitydetectionandunderstandingsystemsinvideointhelasteighteenmonths.InthesesystemsfeaturesextractedfromvideoandassociatedaudioareprocessedbyHiddenMarkovModels(HMMs),NeuralNetworks(NN),DynamicProgramming(DP),andeigens
3、pacemethodstoextractmeaningfulinformation.Inthisreport,thefollowingmethodsaredescribed:-HumanfacedetectioninvideousingDP,-humanbodydetectionusingclassificationofmovingregionsinvideo,-movingobjectclassificationbyeigenanalysisofperiodicmotion,-detectionoffallingperson
4、sinvideousingbothaudioandvideotrackdata.-detectionoffireinvideo,and-detectionofmovingtreeleavesinvideo.Inthesecondpartofthereport,ourresearchonmultimediadatabasesandthepartialsolutionstothefollowingproblemsaredescribed:-Naturallanguageinterfacesforinteractionwithmul
5、timediadatabases,-gesture-basedinteractionwithmultimediadatabases,and-textrecognitioninvideo.Finallygapsinknow-howandrecommendedfuturedirectionsarediscussed.1.HumanFaceDetectioninVideoOurcurrentresearchisbasedontheideathatafacecanberecognizedbyitsedges.Infact,acaric
6、aturistdrawsafaceimageinafewstrokesbydrawingthemajoredgesoftheface.Mostwaveletdomainimageclassificationmethodsarealsobasedonthisfactbecausewaveletcoefficientsarecloselyrelatedwithedges,seee.g.,[Garcia1999].Thefirststepofthealgorithmistofindpossiblefaceregionsinatypi
7、calimageorvideo.Thiscanbedeterminedbydetectingregionswithpossiblehumanskincolors.Intheseregionsedgesareestimatedbyusingastandardedgedetector.Edgescanbeestimatedbysummingtheabsolutevaluesofhigh-low,low-highandhigh-highwaveletsub-imagesofthetwo-dimensionalwavelettrans
8、form.Waveletcoefficientsofthelow-high(high-low)sub-imagecorrespondtohorizontal(vertical)edgesoftheimageregion.Aftercomputingtheedgesinskin
