@Article{XavierBaro2009,
author="Xavier Baro
and Sergio Escalera
and Jordi Vitria
and Oriol Pujol
and Petia Radeva",
title="Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification",
journal="IEEE Transactions on Intelligent Transportation Systems",
year="2009",
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.",
optnote="OR;MILAB;HuPBA;MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1116), last updated on Thu, 19 Dec 2013 12:31:25 +0100",
issn="1524-9050",
doi="10.1109/TITS.2008.2011702"
}