Abstract-This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driverassistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions.

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Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3-5% with a reduction in the number of support vectors of 50-70%.

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