For autonomously acting robots and driver assistance systems powerful optical stereo sensor systems are required. Object positions and environmental conditions have to be acquired in real-time. In this paper an algorithm based on a hardware-software co-design is applied. A depth-map is generated with a hierarchical detection method. A depth-histogram is generated by using the density distribution of the disparity in the depth-map. It is used for object detection. The object clustering can be accomplished without calculation of 3D-points, due to the almost identical mapping of the objects over the whole distance, within the histogram. A lane detection is applied by using a Hough transform. The suitability at night and the detection of small objects like bikers is proven.


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    Titel :

    Real-time vehicle and lane detection with embedded hardware


    Beteiligte:
    Kaszubiak, J. (Autor:in) / Tornow, M. (Autor:in) / Kuhn, R.W. (Autor:in) / Michaelis, B. (Autor:in) / Knoeppel, C. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    993448 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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