Traffic sign detection and identification using SURF algorithm and GPGPU

Traffic sign identification is one of the key components of the Driver Assistant Systems (DAS). It can provide important information for safety driving. In this paper, we propose a method for traffic sign detection and identification. First, potential traffic signs are segmented by color threshold, and a polygon approximation algorithm is used to detect appropriate polygons. The potential signs are compared with the template signs in the database using SURF feature matching method. In the identification step, we apply the SURF algorithm for a CPU only system and a CPU with GPGPU system. Experiment results show that our method works robustly and efficiently for the selected data.