Two‐stage traffic sign detection and recognition based on SVM and convolutional neural networks

Abstract

Nowadays, traffic sign recognition is the most important task of advanced driver assistance systems since it improves the safety and comfort of drivers. However, it remains a challenging task due to the complexity of road traffic scenes. In this study, a novel two-stage approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. The first stage aims to detect and classify the detected traffic signs into circular and triangular shape using HOG features and linear support vector machines (SVMs). The main objective of the second stage is to recognise the traffic signs using a convolutional neural network into their subclasses. The performance of the whole process is tested on German traffic sign detection benchmark (GTSDB) and German traffic sign recognition benchmark (GTSRB) datasets. Experimental results show that the obtained detection and recognition rate is comparable with those reported in the literature with much less complexity. Furthermore, the average processing time demonstrates its suitability for real-time processing applications.