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1、附录AReal-timeobjectrecognitionusinglocalfeaturesonaDSP-basedembeddedsystemAbstractInthelastfewyears,objectrecognitionhasbecomeoneofthemostpopulartasksincomputervision.Inparticular,thiswasdrivenbythedevelopmentofnewpowerfulalgorithmsforlocalappearancebasedo
2、bjectrecognition.So-called‘‘smartcameras’’withenoughpowerfordecentralizedimageprocessingbecamemoreandmorepopularforallkindsoftasks,especiallyinthefieldofsurveillance.Recognitionisaveryimportanttoolastherobustrecognitionofsuspiciousvehicles,personsorobject
3、sisamatterofpublicsafety.Thissimplymakesthedeploymentofrecognitioncapabilitiesonembeddedplatformsnecessary.Inourworkweinvestigatethetaskofobjectrecognitionbasedonstate-of-the-artalgorithmsinthecontextofaDSP-basedembeddedsystem.Weimplementseveralpowerfulal
4、gorithmsforobjectrecognition,namelyaninterestpointdetectortogetherwithanregiondescriptor,andbuildamedium-sizedobjectdatabasebasedonavocabularytree,whichissuitableforourdedicatedhardwaresetup.Wecarefullyinvestigatetheparametersofthealgorithmwithrespecttoth
5、eperformanceontheembeddedplatform.Weshowthatstate-of-the-artobjectrecognitionalgorithmscanbesuccessfullydeployedonnowadayssmartcameras,evenwithstrictlylimitedcomputationalandmemoryresources.KeywordsDSP;Objectrecognition;Localfeatures;Vocabularytree1.Intro
6、ductionObjectrecognitionisoneofthemostpopulartasksinthefieldofcomputervision.Inthepastdecade,bigeffortsweremadetobuildrobustobjectrecognitionsystemsbasedonappearancefeatureswithlocalextent.Forsuchaframeworktobeapplicableintherealworldseveralattributesarev
7、eryimportant:insensitivityagainstrotation,illuminationorviewpointchanges,aswellasreal-timebehaviorandlarge-scaleoperation.Currentsystemsalreadyhavealotofthesepropertiesand,thoughnotallproblemshavebeensolvedyet,nowadaystheybecomemoreandmoreattractivetothei
8、ndustryforinclusioninproductsforthecustomermarket.Inturn,recentlyembeddedvisionplatformssuchassmartcamerashavesuccessfullyemerged,however,onlyofferingalimitedamountofcomputationalandmemoryresources.Nevertheless,embe
