Road-Sign Detection and Recognition Based on Support Vector Machines

Abstract

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.

Detection and recognition of traffic signs inside the attentional visual field of drivers

“…Authors in [15] proposed a new method for detecting white signs by using RGB color space. They used chromatic and achromatic filters in order to help them in detecting white signs.…”

Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition

“…This is then followed by detection and classification using the authors favorite choice of classifier, e.g. support vector machines [12] or Neural networks [5,14]. Another common approach is to consider an edge map using e.g.…”

An Approach to the Recognition of Informational Traffic Signs Based on 2-D Homography and SVMs

“…We refused direct thresholding over RGB color space because, despite it might be faster under certain circunstances, it turns out to be very sensitive to lighting changes. A combination of a fixed HSI segmentation and an achromatic decomposition was consequently chosen due to its benefits as it is explained in [1].…”

“…Several works have recently focused on traffic sign detection and recognition [1][2][3][4][5][6][7][8][9]. Some of which keep stages for sign detection and classification separated, such as [5] and [6], while some others try to address the whole process in a unique framework like [7].…”