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Traffic sign recognition system with β -correction

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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.

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Authors and Affiliations

  1. Department Ciències de la Computació, Computer Vision Center, UAB, 08193, Bellaterra, Spain

    Sergio Escalera & Petia Radeva

  2. Department Matemàtica Aplicada i Anàlisi, UB, Gran Via 585, 08007, Barcelona, Spain

    Oriol Pujol

Authors
  1. Sergio Escalera
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  2. Oriol Pujol
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  3. Petia Radeva
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Correspondence to Sergio Escalera.

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Escalera, S., Pujol, O. & Radeva, P. Traffic sign recognition system with β -correction. Machine Vision and Applications 21, 99–111 (2010). https://doi.org/10.1007/s00138-008-0145-z

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  • DOI: https://doi.org/10.1007/s00138-008-0145-z

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