Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, the authors introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. They classify a wide set of traffic signs types using robust error correcting codings. Moreover, they introduce the novel beta-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. The paper is organized as follows: Section 2 overviews the ECOC coding strategies and presents the novel beta-correction decoding approaches. Section 3 explains the system for traffic signs classification. Section 4 shows experimental results, and finally, section 5 concludes the paper.


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    Titel :

    Traffic sign recognition system with beta-correction


    Weitere Titelangaben:

    Erkennung von Verkehrszeichen mit beta-Korrektur


    Beteiligte:
    Escalera, Sergio (Autor:in) / Pujol, Oriol (Autor:in) / Radeva, Petia (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2010


    Format / Umfang :

    13 Seiten, 14 Bilder, 10 Tabellen, 22 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




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