This paper presents a neural network approach to classify traffic signs based on greyscale images. The developed system runs on a multi-core processor. The optimization of the neural network concerning fix-point arithmetic and memory consumption results in real-time implementation without the requirement of an external memory (low system costs). A parallelization of the processing scheme allows a high utilization of the multi-core processor. The neural network proposed in this paper is trained with computer generated samples of traffic signs. These patterns cover most possible distortions and main environment situations.


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    Titel :

    Classification of traffic signs in real-time on a multi-core processor


    Beteiligte:
    Ach, R. (Autor:in) / Luth, N. (Autor:in) / Schinner, T. (Autor:in) / Techmer, A. (Autor:in) / Walther, S. (Autor:in)


    Erscheinungsdatum :

    2008-06-01


    Format / Umfang :

    940439 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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