一种改进的交通标志图像识别算法

An Improved Traffic Sign Image Recognition Algorithm

交通标志识别(TSR)系统是智能交通系统的重要研究方向。道路交通环境复杂、交通标志数据库规模庞大等因素导致在设计TSR系统可行性方案时必须考虑计算复杂度和识别率。提出了一种高效且快速的基于改进主成分分析(PCA)法和极限学习机(ELM)的TSR算法,被称为PCA-HOG。该算法首先提取交通标志数据库中每个交通标志的梯度方向直方图(HOG)特征,利用改进PCA算法对提取出的HOG特征进行降维处理,之后利用降维后的HOG特征进行ELM模型训练,利用经过训练的ELM模型识别测试图片。实验结果表明,基于PCAHOG和ELM模型的交通标志识别算法获得的计算复杂度低,图像识别率可达97.69%。

The traffic sign recognition （TSR） system is an important research direction in the field of intelligent transport system. Due to traffic complexity, large scale of traffic signs database and other reasons, the feasibility of TSR design must take computational complexity and recognition rate into consideration. An efficient and fast traffic sign algorithm is proposed based on the improved principal component analysis （PCA） and extreme learning machine （ELM）, as known as PCA-ELM. Firstly, the histogram of gradient direction （HOG） features for each TSR are extracted from traffic sign database. HOG dimensional features are reduced by the improved PCA algorithm. ELM model training is presented based on the HOG after dimension reduction. Image recognition is tested based on the trained ELM model. Experimental results show that the recognition algorithm based on PCA-HOG and ELM model can get a high recognition rate of 97.69% and perform low in computational complexity.