Traffic Sign Recognition Using Complementary Features

Traffic sign recognition is difficult due to the low resolution of image, illumination variation and shape distortion. On the public dataset GTSRB, the state-of-the-art performance have been obtained by convolutional neural networks (CNNs), which learn discriminative features automatically to achieve high accuracy but suffer from high computation costs in both training and classification. In this paper, we propose an effective traffic sign recognition method using multiple features which have demonstrated effective in computer vision and are computationally efficient. The extracted features are the histogram of oriented gradients (HOG) feature, Gabor filter feature and local binary pattern (LBP) feature. Using a linear support vector machine (SVM) for classification, each feature yields fairly high accuracy. The combination of three features has shown good complementariness and yielded competitively high accuracy. On the GTSRB dataset, our method reports an accuracy of 98.65%.

I. Introduction

Automatic traffic sign recognition (TSR) has important application in intelligent transportation systems. It is a complex pattern recognition problem, requiring accurate recognition in real-time, because traffic signs are generally detected from live video during fast movement of vehicles. Both traffic sign detection and recognition encounters multitude of difficulties and have attracted high attention of research. Traffic sign recognition is challenging due to the fast changing environment and illumination, cluttered background, low resolution, shape distortion resulted from changing camera angles.