Fast and robust traffic sign recognition is very important but difficult for the safety driving assist systems. This study addresses the fast and robust traffic sign recognition to enhance safety driving. We first adopt the typical Hough transform methods to implement coarse-grained locating of the candidate regions (shapes of rectangle, triangle and circle, etc.) of the traffic signs; and then propose a ring of RIBP (Rotation Invariant Binary Pattern) histograms based feature in Gaussian space to reduce the traffic sign detection time and achieve the robustness on traffic sign detection in terms of scale, rotation, and illumination; Finally, the learning based techniques are used to reduce the feature dimension and implement the classification, which greatly reduce the processing time of traffic sign recognition. Experiments on the GTSRB dataset show that this work achieves 98.62% recognition accuracy and average 0.005 second per image recognition time, which exhibit the comparable recognition accuracy and higher recognition speed comparing to the state-of-the-art works.
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