This study describes an effective method for detecting, tracking and classifying vehicles during night-time in order to support automotive adaptive illumination applications. The hereby described software framework, which computes the relative position, velocity and estimated class of all detected vehicles, integrates multiple processing stages. Firstly, an image segmentation using a threshold method to detect all light sources in the image. Secondly, possible pairs of head- and taillight are clustered using geometrical information. Thirdly, all detected ob-jects are tracked using a Kalman-Filter to increase resolution and robustness of the algorithm. Lastly, a method for computing distance and velocity for all classi-fied objects (e.g. cars, trucks, bikes) is presented. The system is tested to run in realtime and some results and conclusions are offered at the end


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    Title :

    Vehicle detection, classification and position estimation based on monocular video data during night-time


    Contributors:


    Publication date :

    2009


    Size :

    13 Seiten, 12 Bilder, 10 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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