Traffic sign recognition system with beta -correction
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 beta-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.
traffic sign classification multi-class classification error correcting output codes embedding of dichotomizers object recognition adaboost