This paper proposes a robust driving recorder based on-road pedestrian tracking system, which effectively integrates Visual Simultaneous Localization And Mapping (V-SLAM), pedestrian detection, ground plane estimation, and kernel-based tracking techniques. The proposed system systematically detects the pedestrians from recorded video frames and tracks the pedestrians in the V-SLAM inferred 3-D space via a tracking-by-detection scheme. In order to efficiently associate the detected pedestrian frame-by-frame, we propose a novel tracking framework, combining the Constrained Multiple-Kernel (CMK) tracking and the estimated 3-D (depth) information, to globally optimize the data association between consecutive frames. By taking advantage of the appearance model and 3-D information, the proposed system not only achieves high effectiveness but also well handles occlusion in the tracking. Experimental results show the favorable performance of the proposed system which efficiently tracks on-road pedestrian in a moving camera equipped on a driving vehicle.


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    Titel :

    Driving recorder based on-road pedestrian tracking using visual SLAM and Constrained Multiple-Kernel


    Beteiligte:
    Kuan-Hui Lee (Autor:in) / Hwang, Jenq-Neng (Autor:in) / Okapal, Greg (Autor:in) / Pitton, James (Autor:in)


    Erscheinungsdatum :

    01.10.2014


    Format / Umfang :

    892912 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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