This paper presents a multi-cue approach to curb recognition in urban traffic. We propose a novel texture-based curb classifier using local receptive field (LRF) features in conjunction with a multi-layer neural network. This classification module operates on both intensity images and on three-dimensional height profile data derived from stereo vision. We integrate the proposed multi-cue curb classifier as an additional measurement module into a state-of-the-art Kaiman filter-based urban lane recognition system. Our experiments involve a challenging real-world dataset captured in urban traffic with manually labeled ground-truth. We quantify the benefit of the proposed multi-cue curb classifier in terms of the improvement in curb localization accuracy of the integrated system. Our results indicate a 25% reduction of the average curb localization error at real-time processing speeds.


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    Titel :

    Towards multi-cue urban curb recognition


    Beteiligte:
    Enzweiler, Markus (Autor:in) / Greiner, Pierre (Autor:in) / Knoppel, Carsten (Autor:in) / Franke, Uwe (Autor:in)


    Erscheinungsdatum :

    01.06.2013


    Format / Umfang :

    775968 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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