Shape classification algorithm using support vector machines for traffic sign recognition

Authors:
P. Gil-Jiménez;S. Lafuente-Arroyo;S. Maldonado-Bascón;H. Gómez-Moreno
Affiliations:
Dpto. de Teoría de la señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares (Madrid), Spain;Dpto. de Teoría de la señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares (Madrid), Spain;Dpto. de Teoría de la señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares (Madrid), Spain;Dpto. de Teoría de la señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares (Madrid), Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

In this paper, a new algorithm for traffic sign recognition is presented. It is based on a shape detection algorithm that classifies the shape of the content of a sign using the capabilities of a Support Vector Machine (SVM). Basically, the algorithm extracts the shape inside a traffic sign, computes the projection of this shape and classifies it into one of the shapes previously trained with the SVM. The most important advances of the algorithm is its robustness against image rotation and scaling due to camera projections, and its good performance over images with different levels of illumination. This work is part of a traffic sign detection and recognition system, and in this paper we will focus solely on the recognition step.