Abstract
Automatic road-signs recognition is becoming a part of Driver Assisting Systems which role is to increase safety and driving comfort. This paper presents an efficient approach for detecting and recognizing road sign in traffic scene images acquired from a moving vehicle. The developed road sign recognition system is divided into two stages: detection stage to localize signs from a whole image, and classification stage that classifies the detected sign into one of the reference signs. The detection module segments the input image in the YCBCR colour space, and then detects road signs using a shape filtering method. The classification module determines the type of detected road signs using a Multi-layer Perceptron neural networks. An extensive experimentation has shown that the proposed approach is robust enough to detect and recognize road signs under varying translation, rotation and lighting conditions.
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