Road-Sign Detection and Recognition Based on Support Vector Machines

TLDR

Road‑sign detection and recognition are critical for traffic‑sign maintenance and driver‑assistance systems, providing essential information that enhances safety and guides driver behavior. The study proposes an automatic road‑sign detection and recognition system using support vector machines. The system detects and recognizes all Spanish traffic‑sign shapes through a three‑stage pipeline: color‑based segmentation, shape classification with linear SVMs, and content recognition with Gaussian‑kernel SVMs, leveraging the generalization properties of SVMs. Experiments show a high success rate with very few false positives, and the algorithm remains robust to translation, rotation, scale, and partial occlusions.

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 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