PT Journal
AU Xavier Baro
   Sergio Escalera
   Jordi Vitria
   Oriol Pujol
   Petia Radeva
TI Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification
SO IEEE Transactions on Intelligent Transportation Systems
JI TITS
PY 2009
BP 113–126
VL 10
IS 1
DI 10.1109/TITS.2008.2011702
AB 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.
ER