Traffic sign detection by ROI extraction and histogram features-based recognition

We present a traffic sign detection model consisting of two modules. The first module is for ROI (region of interest) extraction. By supervised learning, it transforms the color images to gray images such that the characteristic colors for the traffic signs are more distinguishable in the gray images. It follows shape template matching, where a set of templates for each target category of signs are designed. After that, a set of ROIs are generated. The second module is for recognition. It validates if an ROI belongs to a target category of traffic signs by supervised learning. Local shape and color features are extracted. The supervised learning methods used in the model are SVMs. The overall model is applied on the GTSDB benchmark and achieves 100%, 98.85% and 92.00% AUC (area under the precision-recall curve) for Prohibitory, Danger and Mandatory signs, respectively. The testing speed is 0.4-1.0 second per image on a mainstream PC, which demonstrates the great potential of the proposed model in real-time applications.