Object recognition, i.e. classification of objects into one of several known object classes, generally is a difficult task. In this paper we address the problem of detecting and classifying moving objects in image sequences from traffic scenes recorded with a static camera. In the first step, a statistical, illumination invariant motion detection algorithm is used to produce binary masks of the scene-changes. Next, Fourier descriptors of the shapes from the refined masks are computed and used as feature vectors describing the different objects in the scene. Finally, a feedforward neural net is used to distinguish between humans, vehicles, and background clutter.


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    Title :

    Detection and recognition of moving objects using statistical motion detection and Fourier descriptors


    Contributors:
    Toth, D. (author) / Aach, T. (author)


    Publication date :

    2003-01-01


    Size :

    843110 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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