%0 Journal Article
%T Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification
%A Xavier Baro
%A Sergio Escalera
%A Jordi Vitria
%A Oriol Pujol
%A Petia Radeva
%J IEEE Transactions on Intelligent Transportation Systems
%D 2009
%V 10
%N 1
%@ 1524-9050
%F Xavier Baro2009
%O OR;MILAB;HuPBA;MV
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1116), last updated on Thu, 19 Dec 2013 12:31:25 +0100
%X 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.
%U http://dx.doi.org/10.1109/TITS.2008.2011702
%P 113–126