This paper proposes a segmentation algorithm using deformable template models to segment a vehicle of interest both from the stationary complex background and other moving vehicles in an image sequence. We define a polygonal template to characterize a general model of a vehicle and derive a prior probability density function to constrain the template to be deformed within a set of allowed shapes. We propose a likelihood probability density function which combines motion information and edge directionality to ensure that the deformable template is contained within the moving areas in the image and its boundary coincides with strong edges with the same orientation in the image. The segmentation problem is reduced to a minimization problem and solved by the Metropolis algorithm. The system was successfully tested on 405 image sequences containing multiple moving vehicles on a highway.


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    Title :

    Vehicle segmentation and classification using deformable templates


    Additional title:

    Fahrzeugsegmentierung und Klassifikation mit Hilfe deformierbarer Schablonen


    Contributors:


    Publication date :

    1996


    Size :

    16 Seiten, 33 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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