This paper presents an experimental evaluation of three different traffic sign detection approaches, which detect or localize various types of traffic signs from real-time videos. Specifically, the first approach exploits geometric features to identify traffic signs, while the other two are developed based on SVM (Support Vector Machine) and AdaBoost learning mechanisms. We describe each of the three approaches, conduct a detailed comparison among them, and examine their pros and cons. Our conclusions should lead to useful guidelines for developing a real-time traffic sign detector.

We propose an efficient Voronoi transform algorithm for constructing Voronoi diagrams using segment lists of rows. A significant feature of the algorithm is that it takes segments rather than pixels as the basic units to represent and propagate the nearest neighbor information. The segment lists are dynamically updated as they are scanned. A distance map can then be easily computed from the segment list representation of the Voronoi diagram. Experimental results have demonstrated its high efficiency. Extension of the algorithm to higher dimensions is also discussed.