Real-time traffic sign recognition using color segmentation and SVM

Abstract

Traffic Sign Recognition (TSR) that can automatically notify and warn a vehicle driver is an essential element in the Advanced Driver Assistance System. In this study, we design and implement a real time traffic sign recognition system implemented on Advantech ARK-2121, a small computer mounted on car. The entire process is divided into two parts, the detection step and the classification step. In the detection step, we adopt color filtering, Laplacian and Gaussian filter to enhance an acquired image. Then, we detect the sign based on the contours. The recognition algorithm is accelerated by dividing an input frame into multiple blocks and process them in parallel. We improve the detection accuracy by enhancing input image before the recognition step. The SVM and HOG features are the major techniques in the recognition step. Our detection accuracy is around 91% and the classification accuracy is higher than 98% on the average.