This article addresses the reliable tracking of oncoming traffic at urban intersections from a moving platform with a stereo vision system. Both motion and depth information is combined to estimate the pose and motion parameters of an oncoming vehicle, including the yaw rate, by means of Kalman filtering. Vehicle tracking at intersections is particularly challenging since vehicles can turn quickly. A single filter approach cannot cover the dynamic range of a vehicle sufficiently. We propose a real-time multi-filter approach for vehicle tracking at intersections. A gauge consistency criteria as well as a robust outlier detection method allow for dealing with sudden accelerations and self-occlusions during turn maneuvers. The system is evaluated both on synthetic and real-world data.


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    Title :

    Tracking oncoming and turning vehicles at intersections


    Contributors:


    Publication date :

    2010-09-01


    Size :

    1688582 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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