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%.