Traffic sign detection is a valuable part of future driver support system. In this paper, we present a novel framework to accurately detect traffic signs from a single color image by analyzing geometrical, physical and text/symbol features of traffic signs. First, we utilize an elaborate edge detection algorithm to extract edge map and accurate edge pixel gradient information. Then, we extract 2-D geometric primitives (circles, ellipses, rectangles and triangles) efficiently from image edge map. Third, the candidate traffic sign regions are selected by analyzing the intrinsic color features, which are invariant to different illumination conditions, of each region circumvented by geometric primitives. Finally, a text and symbol detection algorithm is introduced to classify true traffic signs. Experimental results demonstrated the capabilities of our algorithm to detect traffic signs with respect to different size, shape, color and illumination conditions.

In this paper, a new traffic sign detection algorithm and a symbol recognition algorithm are proposed. For a traffic sign detection, a dominant color transform is introduced, which serves as a tool of highlighting a dominant primary color, while discarding the other two primary colors. For a symbol recognition, the curvilinear shape distribution on a circle centered on the centroid of the symbol, called a circular pattern vector, is used as a spatial feature of the symbol. The circular pattern vector is invariant to scaling, translation, and rotation. As simulation results, the effectiveness of traffic sign detection and recognition algorithms are confirmed.