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.
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