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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    Title :

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


    Contributors:
    Ach, R. (author) / Luth, N. (author) / Schinner, T. (author) / Techmer, A. (author) / Walther, S. (author)


    Publication date :

    2008-06-01


    Size :

    940439 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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