This paper describes a vehicle detection method using 3D data derived from a disparity map available in realtime. The integration of a flat road model reduces the search space in all dimensions. Inclination changes are considered for the road model update. The vehicles, modeled as a cuboid, are detected in an iterative refinement process for hypotheses generation on the 3D data. The detection of a vehicle is performed by a mean-shift clustering of plane fitted segments potentially belonging together in a first step. In the second step a u/v-disparity approach generates vehicle hypotheses covering differently appearing vehicles. The system was evaluated in real-traffic-scenes using a Global Positioning System (GPS).


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    Title :

    Stereo vision based vehicle detection


    Contributors:


    Publication date :

    2010


    Size :

    8 Seiten, 18 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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