Ellahyani Ayoub, El Ansari Mohamed, Lahmyed Redouan, Trémeau Alain
J Opt Soc Am A Opt Image Sci Vis. 2018 Nov 1;35(11):1907-1914. doi: 10.1364/JOSAA.35.001907.
Traffic sign recognition is one of the main components of intelligent transportation systems (ITS). It improves safety by informing the driver of the current state of the road, e.g., warnings, prohibitions, restrictions, and other information useful for driving. This paper presents a new road sign recognition method that is achieved in three main steps. The first step maps the input image from the Cartesian coordinate system to the log-polar one. The second step computes the histogram of oriented gradients, local binary pattern, and local self-similarity characteristics from the image represented in the log-polar coordinate system. The third step performs classification on the basis of the random forest classifier and the features computed in the second step. The proposed method has been tested on the German Traffic Sign Recognition Benchmark dataset, and the results obtained are satisfactory when compared to the state-of-the-art approaches.
交通标志识别是智能交通系统（ITS）的主要组成部分之一。它通过向驾驶员告知道路的当前状况（例如警告、禁令、限制以及其他对驾驶有用的信息）来提高安全性。本文提出了一种新的道路标志识别方法，该方法主要通过三个步骤实现。第一步将输入图像从笛卡尔坐标系映射到对数极坐标系。第二步从对数极坐标系中表示的图像计算方向梯度直方图、局部二值模式和局部自相似性特征。第三步基于随机森林分类器和第二步中计算的特征进行分类。所提出的方法已在德国交通标志识别基准数据集上进行了测试，与当前最先进的方法相比，所获得的结果令人满意。
