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, the authors 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. They classify a wide set of traffic signs types using robust error correcting codings. Moreover, they 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. The paper is organized as follows: Section 2 overviews the ECOC coding strategies and presents the novel beta-correction decoding approaches. Section 3 explains the system for traffic signs classification. Section 4 shows experimental results, and finally, section 5 concludes the paper.
Traffic sign recognition system with beta-correction
Erkennung von Verkehrszeichen mit beta-Korrektur
Machine Vision and Applications ; 21 , 2 ; 99-111
2010
13 Seiten, 14 Bilder, 10 Tabellen, 22 Quellen
Aufsatz (Zeitschrift)
Englisch
Traffic sign recognition system with ? -correction
British Library Online Contents | 2010
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