Traffic Sign Recognition Using Dictionary Learning Method

Recent researches have paid more and more attention to traffic sign recognition due to its important role in the intelligence transportation system. In the traditional methods for this task, first the traffic signs are located using the color or shape information of the traffic signs, then a classifier is applied for classification. In this paper, we propose a novel framework using the sparse model for traffic information representation and a classifier using a probability method for classification. Results of experiments using examples from the Caltech 101 Object Categories show that the proposed method is efficient for traffic sign recognition.