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. © 2010 IEEE.