@article{ref1,
title="Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification",
journal="IEEE transactions on intelligent transportation systems",
year="2009",
author="Baro, X. and Escalera, S. and Vitria, J. and Pujol, O. and Radeva, P.",
volume="10",
number="1",
pages="113-126",
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.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2008.2011702",
url="http://dx.doi.org/10.1109/TITS.2008.2011702"
}