Traffic Sign Classification using Support Vector Machine and Image Segmentation

Abstract

Traffic sign classification system is a part of driving assistance system that automatically alerts and instructs the driver about the meaning of traffic signs. In this paper, we proposed the idea for classifying automatically each type of traffic signs. The proposed method has been tested with a publicly available dataset: German Traffic Sign Recognition Benchmark (GTSRB). We use 360 images for testing by grouping traffic signs into 12 groups and images are taken by short distance from camera. In the first stage of our method, we have to separate particular traffic sign from background using color analysis, edge detection, and region of interest (ROI). In the process of ROI, we use Hough Transform algorithm to detect the different shapes of circle, square, and triangle. After we obtained edge images and images from ROI, in the subsequent stage we use 75% of images for training and 25% of images for testing using support vector machine classification algorithm. From ROI image we use feature extraction to get normal direction from edge image and also use support vector machine (SVM) to classify and compare results between ROI and ROI with normal direction feature. In our experimental results, using ROI and SVM can improve the efficiency of classification which has the accuracy 73%, ROI with normal direction feature and SVM can improve the efficiency of classification which has the accuracy 88.60%, when compare with the original involved background 67.80%. The improvement from SVM is about 5% and 20.8% respectively.