URBAN HIGHWAY AUTONOMIC CONTROL SYSTEM (RAMP METERING AND SPEED LIMIT CONTROL AS AUTONOMIC SYSTEM)

Real time vehicle trajectory estimation on multiple lanesKristian Kovai, Edouard Ivanjko, Hrvoje Goldkristian.kovacic@fpz.hr1UNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014University of ZagrebFaculty of Transport and Traffic Sciences1University of ZagrebFaculty of Transport and Traffic Sciences2IntroductionVehicle detectionVehicle trajectory estimationVehicle detection speed upExperimental resultsConclusion and future workOutlineUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014Introduction3New ITS based traffic control approaches need real time dataVideo camera is more and more used for traffic parameters measurementVehicle detection for flow and velocity measurementOrigin-Destination (OD) matricesLicense plate recognition, etc.ProblemTracking vehicles on multiple lanes simultaneously with only one cameraCommercial versions need one camera per laneUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014Vehicle detection4Image importIP camera, video filePreprocessing algorithmNoise reductionGaussian filter with 5x5 matrix

UNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014Vehicle detection5Background substraction method(a) Creation of background image model(b) Detection of foreground objects

UNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014Vehicle detection6Object clustering methodCheck if adjacent pixels existand combine them into clusterIf cluster area A threshold,remove clusterObject tracking methodCompare all objects in thenew frame with objects inthe previous frame andcombine only those withmax w

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Vehicle trajectory estimationUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014Vehicle trajectory estimation7Postprocessing object locationExtended Kalman Filter

Histogram for computing average values of position(x, y), velocity (v), acceleration (a), direction (), angular velocity () based on EKF outputSetting initial values of state vector x by histogram

Measurement vector

State vectorUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014Vehicle detectionSpeed up8Optimization approachExecuting algorithms on GPU as much as possibleAdding support for CPU SIMD instructions to algorithms which are incapable to run on GPUPerforming computations using multiple threadsParallelization of image processing algorithms

UNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 20148Experimental resultsVehicle detection accuracy9ApproachVehicle count per laneTotalLeftRightOverlap checkHits1266561FP / FN0 / 60 / 50 / 1Accuracy95,6%92,9%98,4%Trajectory checkHits1296861FP / FN1 / 40 / 31 / 1Accuracy96,2%95,8%96,8%True vehicle count1327062Vehicle counting approachesCheck if vehicle bounding box / trajectory is overlapping with one of virtual markersUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 20149Experimental resultsVehicle trajectory estimation accuracy10Simulation of 3D road traffic scene with known parametersSynthetic environment designed inAutodesk 3ds MaxNoise added to measured trajectory

UNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 201410Experimental resultsExecution time11Execution time distribution per image processing component

Overall execution time of applicationUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 201411Experimental resultsArised problems12Overlapping vehicles cause false positive and false negative detectionsEnvironment conditions (sun reflection, rapid lighting changes), camera vibrations caused by strong wind or passing of large vehicles

UNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 201412ConclusionFuture work13One camera can be used for multiple lanesVehicle detection and trackingVehicle detection and trajectory estimation in real timeMeasuring traffic flowFirst results promisingVehicle counting accuracy over 95%Future workVehicle classification (bicyclist, car, truck)Possible to use one camera per one road traffic network nodeComputation of origin-destination matrix of a large road traffic network using license plate recognitionUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014University of ZagrebFaculty of Transport and Traffic Sciences14AcknowledgmentThis research has been partially supported by

European Union from the European Regional Development Fund by the project IPA2007/HR/16IPO/001-040514 VISTA - Computer Vision Innovations for Safe TrafficLeading institution University of Zagreb, Faculty of electrical engineering and computing

EU COST action TU1102 - Towards Autonomic Road Transport Support SystemsUNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014Real time vehicle trajectory estimation on multiple lanesKristian Kovai, Edouard Ivanjko, Hrvoje Goldkristian.kovacic@fpz.hr15UNIZG-FTTSCCVW, Zagreb, Croatia, 16 September 2014University of ZagrebFaculty of Transport and Traffic Sciences15