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Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification

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5 Author(s)
Baro, X. ; Comput. Vision Center, Campus Univ. Autonoma de Barcelona, Barcelona ; Escalera, S. ; Vitria, J. ; Pujol, O.
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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.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:10 ,  Issue: 1 )

Date of Publication:

March 2009

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