TRAFFIC SIGN CLASSIFICATION USING ERROR CORRECTING TECHNIQUES

Sergio Escalera, Petia Radeva, Oriol Pujol

Abstract

Traffic sign classification is a challenging problem in Computer Vision due to the high variability of sign appearance in uncontrolled environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a classification technique for traffic signs recognition by means of Error Correcting Output Codes. Recently, new proposals of coding and decoding strategies for the Error Correcting Output Codes framework have been shown to be very effective in front of multiclass problems. We review the state-of-the-art ECOC strategies and combinations of problem-dependent coding designs and decoding techniques. We apply these approaches to the Mobile Mapping problem. We detect the sign regions by means of Adaboost. The Adaboost in an attentional cascade with the extended set of Haar-like features estimated on the integral shows great performance at the detection step. Then, a spatial normalization using the Hough transform and the fast radial symmetry is done. The model fitting improves the final classification performance by normalizing the sign content. Finally, we classify a wide set of traffic signs types, obtaining high success in adverse conditions.

Download


Paper Citation


in Harvard Style

Escalera S., Radeva P. and Pujol O. (2007). TRAFFIC SIGN CLASSIFICATION USING ERROR CORRECTING TECHNIQUES . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 281-285. DOI: 10.5220/0002058102810285


in Bibtex Style

@conference{visapp07,
author={Sergio Escalera and Petia Radeva and Oriol Pujol},
title={TRAFFIC SIGN CLASSIFICATION USING ERROR CORRECTING TECHNIQUES},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={281-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002058102810285},
isbn={978-972-8865-74-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - TRAFFIC SIGN CLASSIFICATION USING ERROR CORRECTING TECHNIQUES
SN - 978-972-8865-74-0
AU - Escalera S.
AU - Radeva P.
AU - Pujol O.
PY - 2007
SP - 281
EP - 285
DO - 10.5220/0002058102810285