Road-Sign Detection and Recognition Based on Support Vector Machines

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 driver-assistance 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

I. Introduction

Traffic-Sign detection and recognition have been an important issue for research recently. Traffic signs have a dual role: First, they regulate the traffic and, second, indicate the state of the road, guiding and warning drivers and pedestrians. These signs can be classified according to their color and shape, and both these characteristics constitute their content, as shown in Table I. The visibility of traffic signs is crucial for the drivers' safety. For example, very serious accidents happen when drivers do not notice a stop sign, as pointed out in [1]. Of course, many other accidents are not related to traffic signs and are due to factors such as the psychological state of drivers. The causes for accidents that are related to traffic signs may be occlusion or partial occlusion of the sign, deterioration of the sign, or possible distraction of the driver. This work is twofold: First, the algorithm that is described here can be used to evaluate the signaling of the road for maintenance purposes, and second, in a future application, it can be used for driver-assistance systems. Table I Meaning of spanish traffic signs according to the color and shape

Color	Shape	Meaning
Red Rim	Circle	Prohibition
Red Rim (Up)	Triangle	Danger
Red Rim (Down)	Triangle	Yield
Red	Octagonal	Stop
Blue	Square	Recommendation
Blue	Circle	Obligation
White	Circle	End of prohibition
Yellow	Circle	End of prohibition (construction)