Dynamic environment interpretation is of special interest for intelligent vehicle systems. It is expected to provide lane information, target depth, and the image positions of targets within given depth ranges. Typical segmentation algorithms cannot solve the problems satisfactorily, especially under the high-speed requirements of a real-time environment. Furthermore, the variation of image positions and sizes of targets creates difficulties for tracking. In this paper, we propose a sensor-fusion method that can make use of coarse target depth information to segment target locations in video images. Coarse depth ranges can be provided by radar systems or by a vision-based algorithm introduced in the paper. The new segmentation method offers more accuracy and robustness while decreasing the computational load.


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    Titel :

    Depth-based target segmentation for intelligent vehicles: fusion of radar and binocular stereo


    Beteiligte:
    Fang, Y. (Autor:in) / Masaki, I. (Autor:in) / Horn, B. (Autor:in)


    Erscheinungsdatum :

    01.09.2002


    Format / Umfang :

    311931 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Depth-Based Target Segmentation for Intelligent Vehicles: Fusion of Radar and Binocular Stereo

    Fang, Y. / Masaki, I. / Horn, B. | British Library Online Contents | 2002



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