AutomaticTvehicleTtrajectoryTextractionTfromTaerialTvideoTdataAdamTBabinec

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Partners5

FacultyTofTInformationTTechnologySTBrnoTUniversityTofTTechnologySTCzechTRepublicTT1TxbabinD4Wstud6fit6vutbr6czTRCETsystemsTs6r6o6STSvatoplukaTČechaTqdSTGq4TDDTBrnoSTCzechTRepublicT1Tadam6babinecWrcesystems6cz

TrafficTMonitoring

RacingTTelemetry

Car:t68tAFtPriaulxSpeed:tBBfFUtkmRhTanFtAccF:tVtUFBUtGLatFtAccF:tfFfWtGSplit:tgtUF6UBtsec

Car:t6UUtYFtMullerSpeed:tU8BFUtkmRhTanFtAccF:tVtUFU8tGLatFtAccF:tfFfUtGSplit:tgtfF994tsec

Improve

IntersectionTDesign

64

ThisT posterT presentsT aT systemT forT automaticT vehicleT trajectoryTextractionTfromTaerialTvideoTdata6TTheT inputTvideoTsequenceTcanTbeTcapturedTbyTregularTactionTcameraTmountedTonTaTmulticopterTdroneTorTballoonTflyingTinTaltitudesTfromTNDTtoT4DDTm6

TheT geo1registrationT ofT videoT sequenceT isT basedT uponTtransformationT estimationT ofT twoT ORBT featureT setsT extractedT fromTaT referenceT imageT andT videoT sequenceT frame6T ToT provideT theTrobustnessT ofT theT estimationST RANSACT procedureT isT employedT toTguideTtheTalgorithm6

VehicleT detectionT candidatesT areT producedT byT videoT sequenceTanalysisT inT temporalT dimensionT toT detectT movingT objects6T ThisT isTcarriedT outT byT backgroundT subtractionT algorithmT usingT GaussianTMixtureT Models6T ItsT outputT isT fusedT togetherT withT aT roadT surfaceTmaskT andT currentlyT trackedT objectsT databaseT toT produceT setT ofTdetectionTcandidates6

Geo1registeredTImage DetectionTCandidates

Detections Multi1ObjectTTracking

InputtPreprocessing Detection Tracking

GIS

InputtVideotFrame

GeoVRegisteredtReferencet

Image

RoadVtReferencet

Mask

GeoVRegisteredtVideotImage

ImagetUndistortion

GeoVRegistration

BackgroundtModel

Classifiers

CandidatetGeneration

StrongtVehicletClassifier

WeaktVehicletClassifier

DetectionVTracktAssociation

TrackstUpdate

TrackstManagement

TheT detectionT candidatesT areT analyzedT byT twoT vehicleT classifiersT 1TstrongT vehicleT classifierT whichT producesT confidentT detectionT cuesSTandTweakTvehicleTclassifierTwhichTprovidesTtrackingTattractorsTtoTaidTmulti1targetT tracking6T TheT classifiersT useT Multi1BlockT LocalT BinaryTPatternT featuresT andT areT constructedT byT AdaBoostT algorithmT onTdatasetTofT4DSDDDThandTannotatedTvehiclesTandTroadTstructures6

TheT trackingT ofT vehiclesT isT carriedT outT inT RGBEEdgeT imageT spaceTusingTaTsetTofT independentTBayesianTbootstrapTparticleT filtersSToneTforTeachTtarget6TTheTtransitionTmodelTofTtheTparticleTfilterT isTsimpleTvelocityTmodelTconsideringTtheTvehicleTpositionTasTintegrationTofTitsTvelocity6TTargetTmodelTofT theTvehicleT isT representedTbyTO4xO4TRGBEEdgeTtemplateTwithTcircularTmaskSTwhichTisTextractedTfromTtheTareaTinitialTvehicleTdetection6TTheTplasticityTofT theTmodelT isTachievedTbyTVzTtemplateTupdateTinTcaseTofToverlappingTstrongTdetectionTandMorTwhenT theT cuesT fromT theT weakT classifierT areT strongT enoughT inT theTareaT ofT trackedT vehicle6T ToT preventT targetT swapsST theT updateT isTdisabledTwhenTmultipleTtargetsToverlap6

TheTevaluationTofTparticleT isTbasedTonT itsT templateTdistanceTtoTtheTtargetT modelT andT theT attractorT cuesT producedT byT theT weakTclassifier6T TheT initialisationT ofT particleT filterT forT movingT objectT isTguidedTbyT,falloff,TalgorithmSTwhichTatTtheTbeginningTofTtheTtargetTtrackingT causesT theTpositionTofT theTparticlesTofTgivenT targetT toTbeTmoreTaffectedTbyTrandomTnoiseTthanTtheirTvelocitySTsoTtheTparticlesTcanT slowlyT adaptT toT theT targetT motionT whileT elevatingT particlespTvelocityTeffectTonTtheirTbehaviour6

qT|TCaptureTAerialTVideo

4T|TProvideTGISTAnnotation

OT|TExtractTTrajectories

NT|TImproveTtheTWorld