Traffic sign recognition is an example of a hard multiclass classification problem. The existing approaches to that problem typically associate with each sign class a real-valued likelihood function and assign such a label to the unknown image that maximizes the value of this function. These template-matching techniques are usually based on arbitrary similarity metrics, such as normalized cross correlation, which do not capture the characteristics of the sign imagery. In this paper, we study the concept of a robust sign similarity measure that can be inferred from the domain-specific data. Two novel machine-learning techniques are proposed as a framework for automatic construction of such a measure from the pairs of images representing either the same or different classes. One is called SimBoost, which is a variation of the AdaBoost algorithm, and the other is based on the fuzzy regression tree framework. Through the experiments with low-quality images, we show that the proposed method admits efficient road sign recognition and outperforms the existing approaches in terms of the classification accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Robust Class Similarity Measure for Traffic Sign Recognition


    Contributors:
    Ruta, A (author) / Yongmin Li, (author) / Xiaohui Liu, (author)


    Publication date :

    2010-12-01


    Size :

    717495 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    MIYASATO KAZUHIRO / KOYASU TOSHIYA | European Patent Office | 2023

    Free access

    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    SHINOMIYA TERUHIKO | European Patent Office | 2017

    Free access