TY  - JOUR
AU  - Xavier Baro
AU  - Sergio Escalera
AU  - Jordi Vitria
AU  - Oriol Pujol
AU  - Petia Radeva
PY  - 2009//
TI  - Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification
T2  - TITS
JO  - IEEE Transactions on Intelligent Transportation Systems
SP  - 113–126
VL  - 10
IS  - 1
N2  - 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.
SN  - 1524-9050
UR  - http://dx.doi.org/10.1109/TITS.2008.2011702
N1  - OR;MILAB;HuPBA;MV
ID  - Xavier Baro2009
ER  -