We propose a multi-layer, real-time vehicle detection and tracking system using stereo vision, multi-view AdaBoost detectors, and optical flow. By adopting a ground plane estimate extracted from stereo information, we generate a sparse set of hypotheses and apply trained AdaBoost classifiers in addition to fast disparity histogramming, for Hypothesis Verification (HV) purposes. Our tracking system employs one Kalman filter per detected vehicle and motion vectors from optical flow, as a means to increase its robustness. An acceptable detection rate with few false positives is obtained at 25 fps with generic hardware.


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    Title :

    Real-time vehicle detection and tracking using stereo vision and multi-view AdaBoost


    Contributors:
    Kowsari, T. (author) / Beauchemin, S. S. (author) / Cho, J. (author)


    Publication date :

    2011-10-01


    Size :

    1106365 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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