This paper presents a method for pedestrian detection and tracking using a night vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road detection module that provides key information for pedestrian validation. Experimental comparisons have been carried out on gray-scale SVM recognition vs. binary SVM recognition and entire body detection vs. upper body detection.


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    Titel :

    Pedestrian detection and tracking with night vision


    Beteiligte:
    Fengliang Xu, (Autor:in) / Kikuo Fujimura, (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2002-01-01


    Format / Umfang :

    900210 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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