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.

中文翻译：

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基于支持向量机和卷积神经网络的两阶段交通标志检测与识别

如今，交通标志识别是高级驾驶员辅助系统的最重要任务，因为它可以提高驾驶员的安全性和舒适性。但是，由于道路交通场景的复杂性，这仍然是一项艰巨的任务。在这项研究中，提出了一种新颖的两阶段方法，用于在实际交通情况下进行实时交通标志检测和识别。第一阶段旨在使用HOG功能和线性支持向量机（SVM）将检测到的交通标志分类为圆形和三角形。第二阶段的主要目标是使用卷积神经网络将交通标志识别为其子类。整个过程的性能在德国交通标志检测基准（GTSDB）和德国交通标志识别基准（GTSRB）数据集上进行了测试。实验结果表明，所获得的检测和识别率与文献报道的相当，且复杂度要低得多。此外，平均处理时间证明了其适用于实时处理应用程序。