Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and traditional blob tracking methods is unable to separate the vehicles in the merged blobs. We use vehicle shape models, in addition to camera calibration and ground plane knowledge, to detect, track and classify moving vehicles in presence of occlusion. We use a 2-stage approach. In the first stage, hypothesis for vehicle types, positions and orientations are formed by a coarse search, which is then refined by a data driven Markov chain Monte Carlo (DDMCMC) process. We show results and evaluations on some real urban traffic video sequence using three types of vehicle models.


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    Titel :

    A model-based vehicle segmentation method for tracking


    Beteiligte:
    Xuefeng Song, (Autor:in) / Nevatia, R. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    385749 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Model-Based Vehicle Segmentation Method for Tracking

    Nevatia, R. / Song, X. / IEEE | British Library Conference Proceedings | 2005


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    Vehicle Segmentation and Tracking in the Presence of Occlusions

    Kanhere, N. K. / Birchfield, S. T. / Sarasua, W. A. et al. | British Library Conference Proceedings | 2006


    Vehicle Segmentation and Tracking in the Presence of Occlusions

    Kanhere, Neeraj K. | Online Contents | 2006


    Vehicle Segmentation and Tracking in the Presence of Occlusions

    Kanhere, Neeraj / Birchfield, Stanley / Sarasua, Wayne | Transportation Research Record | 2006