The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.


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    Titel :

    Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification


    Beteiligte:
    Baro, X. (Autor:in) / Escalera, S. (Autor:in) / Vitria, J. (Autor:in) / Pujol, O. (Autor:in) / Radeva, P. (Autor:in)


    Erscheinungsdatum :

    01.03.2009


    Format / Umfang :

    978225 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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