PT Journal
AU Sergio Escalera
   Oriol Pujol
   Petia Radeva
TI Traffic sign recognition system with β -correction
SO Machine Vision and Applications
JI MVA
PY 2010
BP 99–111
VL 21
IS 2
DI 10.1007/s00138-008-0145-z
AB 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.
ER