Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification

  • Authors:

  • Xavier Baró;Sergio Escalera;Jordi Vitrià;Oriol Pujol;Petia Radeva

  • Affiliations:

  • Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain;Computer Vision Center, Barcelona, Spain and Department Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain;Computer Vision Center, Barcelona, Spain and Department Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain;Computer Vision Center, Barcelona, Spain and Department Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain;Computer Vision Center, Barcelona, Spain and Department Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain

  • Venue:
  • IEEE Transactions on Intelligent Transportation Systems
  • Year:
  • 2009

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Abstract

The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.