Traffic sign recognition system with β -correction

Sergio Escalera, Oriol Pujol, Petia Radeva

    Research output: Contribution to journalArticleResearchpeer-review

    17 Citations (Scopus)

    Abstract

    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, we 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. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. © 2008 Springer-Verlag.
    Original languageEnglish
    Pages (from-to)99-111
    JournalMachine Vision and Applications
    Volume21
    Issue number2
    DOIs
    Publication statusPublished - 1 Feb 2010

    Keywords

    • Adaboost
    • Embedding of dichotomizers
    • Error correcting output codes
    • Multi-class classification
    • Object recognition
    • Traffic sign classification

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