Road-Sign Detection and Recognition Based on Support Vector Machines

• Published in: IEEE transactions on intelligent transportation systems Vol. 8; no. 2; pp. 264 - 278
• Main Authors: Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., Lopez-Ferreras, F.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01-06-2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Classification; Color; Computer science; control theory; systems; Control theory. Systems; Data processing. List processing. Character string processing; detection; Drivers; Exact sciences and technology; Gaussian processes; Ground, air and sea transportation, marine construction; hue; hue saturation intensity (HSI); Invariants; Layout; Memory organisation. Data processing; Pattern recognition. Digital image processing. Computational geometry; Psychology; Recognition; Road accidents; road sign; Road vehicles; Robotics; Safety; Segmentation; Shape; Shape recognition; Signs; Software; Street signs; Studies; Support vector machine classification; Support vector machines; support vector machines (SVMs); Testing; Occlusion; Invariant; hue saturation intensity (HSI); Segmentation; Image processing; road sign; Visual system; Classification; Vector support machine; User assistance; Probability learning; Traffic information systems; Computer vision; detection; Statistical analysis; Vehicle driving; Maintenance; Road signalling; Color image; Kernel method; Gaussian process; Automatic measurement; hue; Occultation; support vector machines (SVMs)
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2007.895311

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