TLD算法ppt课件

ObjectTrackingwithTracking Learning Detection apsvvfb 1 Tracking Learning Detection 2 return location locations Objectlocation 3 Tracking Learning Detection P NLearningObjectModelObjectDetectionTrackerIntegratorLearningComponent 4 PN learning Aclassifiertobelearnedtrainingset acollectionoflabeledtrainingexamplessupervisedtraining amethodthattrainsaclassifierfromtrainingset iv P Nexperts 5 PN learning P expert analyzesexamplesclassifiedasnegative estimatesfalsenegativesandaddsthemtotrainingsetwithpositivelabel N expert analyzesexamplesclassifiedaspositive estimatesfalsepositivesandaddsthemwithnegativelabeltothetrainingset falsepositives falsenegatives 6 PN learning P precisionP recallN precisionN recall 7 PN learning Afterdefiningthestatevectoranda2x2matrixMas Itispossibletorewritetheequationsas 8 PN learning TheP Nexpertsarecharacterizedbyfourqualitymeasures Toreducethis4Dspace theparametersaresettowhererepresentserroroftheexpert Thetransitionmatrixthenbecomes Theeigenvaluesofthismatrixare ThereforetheP Nlearningshouldbeimprovingtheperformanceif Theerrorisvariedintherange 9 PN learning 10 PN learning ThestatevectorconvergestozeroifbotheigenvaluesofthetransitionmatrixMaresmallerthanone 11 PN learning designofrealexperts Ineveryframe theP Nlearningperformsthefollowingsteps evaluationofthedetectoronthecurrentframeestimationofthedetectorerrorsusingtheP Nexpertsupdateofthedetectorbylabeledexamplesoutputbytheexperts 12 PN learning designofrealexperts structureP expert assumesthattheobjectmovesalongatrajectory N expert assumesthattheobjectcanappearatasinglelocationonly 13 PN learning Thecaristrackedfromframetoframebyatracker ThetrackerrepresentstheP expertthatoutputspositivetrainingexamples Noticethatduetoocclusionoftheobject theoutputofP expertintimet 2outputsincorrectpositiveexample N expertidentifiesmaximallyconfidentpatch denotedbyaredstar andlabelsallotherdetectionsasnegative 14 Tracking Learning Detection P NLearningObjectModelObjectDetectionTrackerIntegratorLearningComponent 15 ObjectModel ObjectmodelMisadatastructurethatrepresentstheobjectanditssurroundingobservedsofar Similaritybetweentwopatchesisdefinedas 16 ObjectModel NearestNeighbor NN classifierApatchpisclassifiedaspositiveifotherwisethepatchisclassifiedasnegative Aclassificationmarginisdefinedas 17 ObjectModel Modelupdate i thepatch slabelestimatedbyNNclassifierisdifferentfromthelabelgivenbytheP Nexperts ii patcheswheretheclassificationmarginissmallerthan 18 Tracking Learning Detection P NLearningObjectModelObjectDetectionTrackerIntegratorLearningComponent 19 ObjectDetector Scanning windowgridscalesstep 1 2 horizontalstep 10 ofwidth verticalstep 10 ofheight minimalboundingboxsize 20pixelsCascadedclassifier 20 ObjectDetector Patchvariance Thisstagerejectsallpatches forwhichgray valuevarianceissmallerthan50 ofvarianceofthepatchthatwasselectedfortracking Thestageexploitsthefactthatgray valuevarianceofapatchpcanbeexpressedas andthattheexpectedvalueE p canbemeasuredinconstanttimeusingintegralimages patch 0 y x 21 ObjectDetector Ensembleclassifer Theposteriorsofindividualbaseclassifiersareaveragedandtheensembleclassifiesthepatchastheobjectiftheaverageposteriorislargerthan50 22 ObjectDetector Ensembleclassifer TheensembleconsistsofTbaseclassifiers Eachbaseclassifieriperformsanumberofpixelcomparisonsonthepatchresultinginabinarycodex 23 ObjectDetector Nearestneighborclassifier Afterfilteringthepatchesbythevariancefilterandtheensembleclassifier wearetypicallyleftwithseveralofboundingboxesthatarenotdecidedyet Therefore wecanusetheonlinemodelandclassifythepatchusingaNNclassifier Apatchisclassifiedastheobjectif 24 Tracking Learning Detection P NLearningObjectModelObjectDetectionTrackerIntegratorLearningComponent 25 Tracker Median Flow pyramidalLucas KanadeTracker Thetrackerestimatesdisplacementsofanumberofpointswithintheobject sboundingbox estimatestheirreliability andvoteswith50 ofthemostreliabledisplacementsforthemotionoftheboundingboxusingmedian Failuredetection Aresidualofasingledisplacementisdefinedas Afailureofthetrackerisdeclaredifmedianpixel 26 Tracking Learning Detection P NLearningObjectModelObjectDetectionTrackerIntegratorLearningComponent 27 Integrator IntegratorcombinestheboundingboxofthetrackerandtheboundingboxesofthedetectorintoasingleboundingboxoutputbyTLD Ifneitherthetrackernotthedetectoroutputaboundingbox theobjectisdeclaredasnotvisible Otherwisetheintegratoroutputsthemaximallyconfidentboundingbox using return 28 Tracking Learning Detection P NLearningObjectModelObjectDetectionTrackerIntegratorLearningComponent 29 Learningcomponent InitializationP expertN expert 30 Learningcomponent 31 Learningcomponent Initialization1 theinitalboundingbox10boundingboxes10 x20positivepatches2 Negativepatchesarecollectedfromthesurroundingoftheinitializingboundingbox 32 Learningcomponent IllustrationofP expert a objectmodelandthecoreinfeaturespace grayblob b unreliable dotted andreliable thick trajectory c theobjectmodelandthecoreaftertheupdate Reddotsarepositiveexamples blackdotsarenegative cross x denotesendofatrajectory P expertThegoalofP expertistodiscovernewappearancesoftheobjectandthusincreasegeneralizationoftheobjectdetector 33 Learningcomponent N expertItsgoalistodiscoverclutterinthebackgroundagainstwhichthedetectorshoulddiscriminate Thekey