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Authors: Sergio Escalera 1 ; Petia Radeva 1 and Oriol Pujol 2

Affiliations: 1 Computer Vision Center, Spain ; 2 UB, Spain

Keyword(s): Traffic Sign Classification, Error Correcting Output Codes, Ensemble of Dichotomies, Multiclass Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

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. (More)

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Paper citation in several formats:
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 (VISIGRAPP 2007) - Volume 2: VISAPP; ISBN 978-972-8865-74-0; ISSN 2184-4321, SciTePress, pages 281-285. DOI: 10.5220/0002058102810285

@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 (VISIGRAPP 2007) - Volume 2: VISAPP},
year={2007},
pages={281-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002058102810285},
isbn={978-972-8865-74-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications (VISIGRAPP 2007) - Volume 2: VISAPP
TI - TRAFFIC SIGN CLASSIFICATION USING ERROR CORRECTING TECHNIQUES
SN - 978-972-8865-74-0
IS - 2184-4321
AU - Escalera, S.
AU - Radeva, P.
AU - Pujol, O.
PY - 2007
SP - 281
EP - 285
DO - 10.5220/0002058102810285
PB - SciTePress