2023年全國碩士研究生考試考研英語一試題真題（含答案詳解+作文范文）_第1頁
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1、ActionRecognitionwithImprovedTrajectiesHengWangCdeliaSchTocitethisversion:HengWangCdeliaSch.ActionRecognitionwithImprovedTrajecties.ICCV2013IEEEInternationalConferenceonComputerVisionDec2013SydneyAustralia.IEEEpp.3551355

2、82013.HALId:hal00873267:hal.inria.frhal00873267v2Submittedon16Oct2013HALisamultidisciplinaryopenaccessarchivefthedepositdisseminationofscientificresearchdocumentswhethertheyarepublishednot.Thedocumentsmaycomefromteaching

3、researchinstitutionsinFranceabroadfrompublicprivateresearchcenters.L’archiveouvertepluridisciplinaireHALestdestineeaudep?otet`aladiffusiondedocumentsscientifiquesdeniveaurecherchepubliesounonemanantdesetablissementsd’ens

4、eignementetderecherchefrancaisouetrangersdeslabatoirespublicsouprives.Figure2.Visualizationofinliermatchesoftherobustlyestimatedhomography.GreenarrowscrespondtoSURFdematchesredonestodenseopticalflow.vanttrajectiesintheba

5、ckgroundinrealisticvideos.Wecanprunethemonlykeeptrajectiesfromhumansobjectsofinterestifweknowthecameramotion(seeFigure1).Furthermegiventhecameramotionwecancrecttheopticalflowsothatthemotionvectsofhumanactsareindependento

6、fcameramotion.Thisimprovestheperfmanceofmotiondesbasedonopticalflowi.e.HOF(histogramsofopticalflow)MBH.WeillustratethedifferencebetweentheiginalcrectedopticalflowinthedletworowsofFigure1.Veryfewapproachesconsidercameramo

7、tionwhenextractingfeaturetrajectiesfactionrecognition.Uemuraetal.[38]combinefeaturematchingwithimagesegmentationtoestimatethedominantcameramotionthenseparatefeaturetracksfromthebackground.Wuetal.[42]applyalowrankassumpti

8、ontodecomposefeaturetrajectiesintocamerainducedobjectinducedcomponents.RecentlyParketal.[27]perfmweakstabilizationtoremovebothcameraobjectcentricmotionusingcoarsescaleopticalflowfpedestriandetectionposeestimationinvideo.

9、Jainetal.[14]decomposevisualmotionintodominantresidualmotionsbothfextractingtrajectoriescomputingdes.AmongtheapproachesimprovingdensetrajectiesVigetal.[39]proposetousesaliencymappingalgithmstoprunebackgroundfeatures.This

10、resultsinamecompactvideorepresentationimprovesactionrecognitionaccuracy.Jiangetal.[15]clusterdensetrajectiesusetheclustercentersasreferencepointssothattherelationshipbetweenthemcanbemodeled.Therestofthepaperisganizedasfo

11、llows.Insection2wedetailourapproachfcameramotionestimationdiscusshowtoremoveinconsistentmatchesduetohumans.Experimentalsetupevaluationprotocolsareexplainedinsection3experimentalresultsinsection4.Thecodetocomputeimprovedt

12、rajectiesdesisavailableonline.11:lear.inrialpes.fr?wangimproved_trajectiesFigure3.Examplesofremovedtrajectiesundervariouscameramotionse.g.panzoomtilt.Whitetrajectiesareconsideredduetocameramotion.Thereddotsarethetrajecty

13、positionsinthecurrentframe.Thelastrowshowstwofailurecases.Theleftoneisduetoseveremotionblur.Therightonefitsthehomographytothemovinghumansastheydominatetheframe.2.ImprovingdensetrajectiesInthissectionwefirstdescribethemaj

14、stepsofourcameramotionestimationmethodhowtouseittoimprovedensetrajecties.Wethendiscusshowtoremovepotentiallyinconsistentmatchesbasedonhumanstoobtainarobusthomographyestimation.2.1.CameramotionestimationToestimatethegloba

15、lbackgroundmotionweassumethattwoconsecutiveframesarerelatedbyahomography[37].Thisassumptionholdsinmostcasesastheglobalmotionbetweentwoframesisusuallysmall.Itexcludesindependentlymovingobjectssuchashumansvehicles.Toestima

16、tethehomographythefirststepistofindthecrespondencesbetweentwoframes.Wecombinetwoapproachesindertogeneratesufficientcomplementarycidatematches.WeextractSURF[3]featuresmatchthembasedonthenearestneighbrule.Thereasonfchoosin

17、gSURFfeaturesistheirrobustnesstomotionblurasshowninarecentevaluation[13].Wealsosamplemotionvectsfromtheopticalflowwhichprovidesuswithdensematchesbetweenframes.Hereweuseanefficientopticalflowalgithmbasedonpolynomialexpans

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