TY  - JOUR
AU  - Sergio Escalera
AU  - Oriol Pujol
AU  - Petia Radeva
PY  - 2010//
TI  - Traffic sign recognition system with β -correction
T2  - MVA
JO  - Machine Vision and Applications
SP  - 99–111
VL  - 21
IS  - 2
PB  - Springer-Verlag
N2  - Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.
SN  - 0932-8092
UR  - http://dx.doi.org/10.1007/s00138-008-0145-z
N1  - MILAB;HUPBA
ID  - Sergio Escalera2010
ER  -