基于DSP的通过局部特征实时物体识别嵌入式系统xWord文件下载.docx

基于DSP的通过局部特征实时物体识别嵌入式系统xWord文件下载.docx

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Abstract

Inthelastfewyears,objectrecognitionhasbecomeoneofthemostpopulartasksincomputervision.Inparticular,thiswasdrivenbythedevelopmentofnewpowerfulalgorithmsforlocalappearancebasedobjectrecognition.So-called‘‘smartcameras’’withenoughpowerfordecentralizedimageprocessingbecamemoreandmorepopularforallkindsoftasks,especiallyinthefieldofsurveillance.Recognitionisaveryimportanttoolastherobustrecognitionofsuspiciousvehicles,personsorobjectsisamatterofpublicsafety.Thissimplymakesthedeploymentofrecognitioncapabilitiesonembeddedplatformsnecessary.Inourworkweinvestigatethetaskofobjectrecognitionbasedonstate-of-the-artalgorithmsinthecontextofaDSP-basedembeddedsystem.Weimplementseveralpowerfulalgorithmsforobjectrecognition,namelyaninterestpointdetectortogetherwithanregiondescriptor,andbuildamedium-sizedobjectdatabasebasedonavocabularytree,whichissuitableforourdedicatedhardwaresetup.Wecarefullyinvestigatetheparametersofthealgorithmwithrespecttotheperformanceontheembeddedplatform.Weshowthatstate-of-the-artobjectrecognitionalgorithmscanbesuccessfullydeployedonnowadayssmartcameras,evenwithstrictlylimitedcomputationalandmemoryresources.

Keywords DSP;

Objectrecognition;

Localfeatures;

Vocabularytree

1.Introduction

Objectrecognitionisoneofthemostpopulartasksinthefieldofcomputervision.Inthepastdecade,bigeffortsweremadetobuildrobustobjectrecognitionsystemsbasedonappearancefeatureswithlocalextent.Forsuchaframeworktobeapplicableintherealworldseveralattributesareveryimportant:

insensitivityagainstrotation,illuminationorviewpointchanges,aswellasreal-timebehaviorandlarge-scaleoperation.Currentsystemsalreadyhavealotofthesepropertiesand,thoughnotallproblemshavebeensolvedyet,nowadaystheybecomemoreandmoreattractivetotheindustryforinclusioninproductsforthecustomermarket.

Inturn,recentlyembeddedvisionplatformssuchassmartcamerashavesuccessfullyemerged,however,onlyofferingalimitedamountofcomputationalandmemoryresources.Nevertheless,embeddedvisionsystemsarealreadypresentinoureverydaylife.Almosteveryone’smobilephoneisequippedwithacameraand,thus,canbetreatedasasmallembeddedvisionsystem.Clearlythisgivesrisetonewapplications,likenavigationtoolsforvisuallyimpairedpersons,orcollaborativepublicmonitoringusingmillionsofartificialeyes.Inaddition,thelowpriceofdigitalsensorsandtheincreasedneedforsecurityinpublicplaceshasledtoatremendousgrowthinthenumberofcamerasmountedforsurveillancepurposes.Theyhavetobesmallinsizeandhavetoprocessthehugeamountsofavailabledataonsite.Furthermore,theyhavetoperformdedicatedoperationsautomaticallyandwithouthumaninteraction.Notonlyinthefieldofsurveillance,butalsointheareasofhouseholdrobotics,entertainment,militaryandindustrialrobotics,embeddedcomputervisionplatformsarebecomingmoreandmorepopularduetotheirrobustnessagainstenvironmentaladversities.EspeciallyDSP-basedembeddedplatformsareverypopularastheyarepowerfulandcheapCPUs,whicharestillsmallinsizeandefficientintermsofpowerconsumption.AsDSPofferthemaximuminflexibilityofthesoftwaretoberun,comparedtootherembeddedunitslikeFPGAs,ASICorGPU,theircurrentsuccessisnotsurprising.

Forthereasonsalreadymentioned,recognitiontasksareaveryimportantareaofresearch.However,inthisrespectsomeattributesofembeddedplatformsstrictlylimitthepracticabilityofcurrentstate-of-the-artapproaches.Forexample,theamountofmemoryavailableonadevicestrictlylimitsthenumberofobjectsinthedatabase.Therefore,forbuildinganembeddedobjectrecognitionsystem,onegoalistomaketheamountofdatatorepresentasingleobjectassmallaspossibleinordertomaximizethenumberofrecognizableobjects.Anotherimportantaspectisthereal-timecapabilityofthesesystems.Algorithmshavetobefastenoughtobeoperationalintherealworld.Theyhavetoberobustanduser-friendly;

otherwise,aproductequippedwithsuchfunctionalityissimplyunattractivetoapotentialcustomer.Forexample,inaninteractivetourthroughamuseum,objectrecognitiononamobiledevicehastobefastenoughtoallowforcontinuityinguidance.Formallyspeaking,weconsiderthistobeanapplicationrequiringsoftreal-timesystembehavior.Clearly,thisisjustoneexample,andtheexactmeaningofthetermreal-timeisdependentontheconcreteapplication.Westillconsideranobjectrecognitionsystemasbeingreal-

timecapable,ifitisabletodeliveratleastoneresultpersecond.Thisalreadyservesenoughformanyapplicationsliketheexampleoftheinteractivemuseumintroducedabove.However,itisclearthatthisdefinitiondoesnotmeetotherapplications,andthatanimprovementinthroughputisneededforobjectrecognitionatframerate,forinstanceincombinationwithobjecttracking.Tosummarize,buildingafull-featuredrecognitionsystemonanembeddedplatformturnsouttobeachallengingproblemgivenallthedifferentaspectsandenvironmentalrestrictionstoconsider.

Inthiswork,wedescribeamethodtodeployamediumsizedobjectrecognitionsystemonaprototypicalDSPbasedembeddedplatform.Tothebestofourknowledge,wearethefirsttoextensivelyinvestigateissuesrelatedtoobjectrecognitioninthecontextofEmbeddedSystems;

bynowthisistheonlyworkstudyingtheinfluenceofvariousparametersonrecognitionperformanceandruntimebehavior.Wepickasetofhigh-levelalgorithmstodescribeobjectsbyasetofappearancefeatures.AsaprototypicallocalfeaturebasedrecognitionsystemweusedifferenceofGaussian（DOG）keypointsandprincipalcomponentanalysisscaleinvariantfeaturetransform（PCASIFT）descriptorstobuildcompactobjectrepresentations.Byarrangingthisinformationinaclevertreelikedatastructurebasedonk-meansclustering,aso-calledvocabularytree,real-timebehaviorisachieved.Byapplyingadedicatedcompressionmechanism,thesizeofthedatastructurecanbetradedoffagainsttherecognitionperformanceandtherebyaccuratetuningthepropertiesofarecognitionsystemtoagivenhardwareplatformcanbeperformed.Asitisshowninextensiveevaluationsbyconsideringboth,specialpropertiesofthealgorithmsanddedicatedadvantagesofspecialhardware,considerablegainsinrecognitionperformanceandthroughputcanbeachieved.

Theremainderofthispaperisstructuredasfollows.InSect.2wegiveanoverviewaboutdevelopmentsinbothareasthatwearebringingtogetherinourwork.Ontheonehandwelistanumberofreferencesinthecontextofobjectrecognitionbycomputervision;

ontheotherhand,weciteanumberofpublicationsfromtheareaofembeddedsmartsensors.Adetaileddescriptionofthemethodsinvolvedinbuildingourobjectrecognitionalgorithmisgiveninpart3.InSect.4weoutlineourframeworkandgivedetailsabouttrainingandimplementationofoursystem.Wecloselydescribeallstepsindesigningourapproachandgivesidenotesonalternativemethods.InSect.5,weexperimentallyevaluateoursystemonachallengingobjectdatabaseanddiscussrealtimeandreal-worldissues.Furthermore,weinvestigatesomespecialfeaturesofourapproachandelucidatethedependenciesofseveralparametersontheoverallsystemperformance.TheworkconcludeswithsomefinalnotesandanoutlookonfutureworkinSect.6.

2.RelatedWork

Inthefollowingwewillgiveashortintroductiontothetopicoflocalfeaturebasedobjectrecognition.Duetothehugeamountofliteratureavailable,wewillfocusonthemostpromisingapproachesusinglocalfeatures,andrefertothosealgorithmswhicharesomehowrelatedtoourwork.Wewillalsogiveashortoverviewaboutobjectrecognitioninthecontextofembeddedsystems,which,duetothesparsenessofexistingapproaches,containbothglobalandlocalmethods,aswellasalgorithms

implementonFPGAandDSP-basedplatforms.

Local-appearancebasedvisualobjectrecognitionbecamepopularafterthedevelopmentofpowerfulinterestregiondetectorsanddescriptors.Earlyfull-featuredobjectrecognitionsystemsdealingwithalltheindividualalgorithmicstepsandtheirrelatedproblemswereproposedbySchmidandMohr,andSchieleandCrowley.Themainideabehindlocalfeaturebasedobjectrecognitionismaintainingobjectrepresentationsfromcollectionsoflocallysampleddescriptions.Inotherwords,theappearanceoflocalpartsofasingleobjectisencodedindescriptors,andasetofthesedescriptorsformsthefinalobjectrepresentation.Forfindingthedistinguishableregions,so-calledinterestregiondetectorsareused,whichfindregionsorpointsofspecialvisualdistinctiveness.Theneighborhoodofsuchregionsissubsequentlyencodedusingaspecialtransformtobuildadescriptioninherentlyprovidingseveraldesirableproperties.Besideinsensitivityagainstilluminationchangesandpartialviewpointinvariance,representationsassetsoflocaldescriptorsofferrobustnessagainstbackgroundclutterandpartialocclusions.Needlesstosaythatasocalledbagofdescriptorsrepresentationcanbebuiltusingonesingleorseveralcombinationsofdifferentdetectorsanddescriptors.

Thecollectivityofalldescriptorsfrommultipleobjects（i.e.,bagsofdescriptors）isusedtobuildadatabase.Giventhisdatabaseandanewrepresentationofanobjecttobe