IEEE Trans Cybern. 2017 Apr;47(4):920-933. doi: 10.1109/TCYB.2016.2533424. Epub 2016 Mar 14.
This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.
本文提出了一种用于交通标志识别（TSR）的计算高效方法。该方法由两个模块组成：1）提取方向梯度直方图变体（HOGv）特征，2）由极端学习机（ELM）算法训练的单个分类器。所提出的 HOGv 特征在冗余和局部细节之间保持良好的平衡，从而可以更好地表示独特的形状。分类器是一个单隐藏层前馈网络。基于 ELM 算法，输入和隐藏层之间的连接实现了随机特征映射，而仅训练隐藏层和输出层之间的权重。因此，不需要逐层调整。同时，输出权重的范数包含在代价函数中。因此，基于 ELM 的分类器可以为多类 TSR 实现最优和通用的解决方案。此外，它可以平衡识别准确性和计算成本。本文使用三个数据集，包括德国 TSR 基准数据集、比利时交通标志分类数据集和修订的映射和评估交通基础设施状态（修订的 MASTIF）数据集，来评估所提出的方法。实验结果表明，所提出的方法不仅在这三个数据集的训练和识别过程中获得了很高的识别准确率，而且还具有极高的计算效率。
