Traffic Sign Recognition Using Perturbation Method

Automatic traffic sign recognition (TSR) expects high accuracy and speed for real-time applications in intelligent transportation systems. Convolutional neural networks (CNNs) have yielded state-of-the-art performance on the public dataset GTSRB, but involve intensive computation. In this paper, we propose a traffic sign recognition method using computationally efficient feature extraction and classification techniques, and using the perturbation strategy to improve the accuracy. On the GTSRB dataset, using gradient direction histogram feature and learning vector quantization (LVQ) classifier achieves a test accuracy 98.48%. Using simple perturbation operations of image translation, the accuracy is improved to 98.88%. The accuracy is higher than that of single CNN and the speed is much higher.