Abstract
Traffic sign detection and recognition system is becoming an essential component of smart cars. Speed-Limit Sign (SLS) is one of the most important traffic signs, since it is used to regulate the speed of vehicles in downtown and highways. The recognition of SLS by drivers is mandatory. In this paper, we investigate SLS detection and recognition system. We focus on North-American speed limit signs, including Canadian and U.S. signs. A modified version of Histogram of Oriented Gradients (HOG) is used to detect and recognize SLS through a set of two-level SVM-based classifiers. Moreover, we build our online database called North-American Speed Limit Signs (NASLS) which includes four SLS categories; white, yellow, black and orange signs. We show through an extensive set of experiments that our system achieves an accuracy of more than 94% of SLS recognition.
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