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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    Title :

    A model-based vehicle segmentation method for tracking


    Contributors:
    Xuefeng Song, (author) / Nevatia, R. (author)


    Publication date :

    2005-01-01


    Size :

    385749 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Model-Based Vehicle Segmentation Method for Tracking

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    Vehicle Segmentation and Tracking in the Presence of Occlusions

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