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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    Titel :

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


    Beteiligte:
    Toth, D. (Autor:in) / Aach, T. (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    843110 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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