In this paper a feature and model based approach to real-time vehicle tracking and classification is described. We proceed in two steps: 1) we establish correspondence between model and image features by an optimization algorithm; and 2) based on this correspondence, a matching vector is derived and used as input to either a Bayes classifier, a neural net or a combination of both. The current implementation updates the model parameters (position and scale) at a rate of 8-12 frames per second.


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    Title :

    Real-time vehicle tracking and classification


    Contributors:
    Noll, D. (author) / Werner, M. (author) / von Seelen, W. (author)


    Publication date :

    1995-01-01


    Size :

    716995 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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