激光与光电子学进展, 2017, 54 (2): 021001, 网络出版: 2017-02-10   

一种改进的交通标志图像识别算法 下载: 936次

An Improved Traffic Sign Image Recognition Algorithm
作者单位
天津大学电子信息工程学院, 天津 300072
摘要
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PCA-HOG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%。
Abstract
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.
参考文献

[1] Song W J, Fu M Y, Yang Y. An efficient traffic signs recognition method for autonomous vehicle[J]. Robot, 2015, 37(1): 102-111.

[2] 李厚杰, 邱天爽, 宋海玉, 等. 基于曲率尺度空间角点检测的交通标志分离算法[J]. 光学学报, 2015, 35(1): 0115002.

    Li Houjie, Qiu Tianshuang, Song Haiyu, et al. Separation algorithm of traffic signs based on curvature scale space corner detection[J]. Acta Optica Sinica, 2015, 35(1): 0115002.

[3] 赵春晖, 尤 伟, 齐 滨, 等. 采用多项式递归核的高光谱遥感异常实时检测算法[J]. 光学学报, 2016, 36(2): 0228002.

    Zhao Chunhui, You Wei, Qi Bin, et al. Real-time anomaly detection algorithm for hyperspectral remote sensing by using recursive polynomial kernel function[J]. Acta Optica Sinica, 2016, 36(2): 0228002.

[4] 郭鹏宇, 苏 昂, 张红良, 等. 结合纹理和形状特征的在线混合随机朴素贝叶斯视觉跟踪器[J]. 光学学报, 2015, 35(3): 0315002.

    Guo Pengyu, Su Ang, Zhang Hongliang, et al. Online mixture of random nave bayes tracker combined texture with shape feature[J]. Acta Optica Sinica, 2015, 35(3): 0315002.

[5] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.

[6] Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61(4): 85-117.

[7] Li M, Yuan B. 2D-LDA: A statistical linear discriminant analysis for image matrix[J]. Pattern Recognition Letters, 2005, 26(5): 527-532.

[8] Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods[M]. New York: Cambridge University Press, 2001: 1-28.

[9] Zaklouta F, Stanciulescu B, Hamdoun O. Traffic sign classification using K-d trees and random forests[C]. Proceedings of 2011 IEEE International Joint Conference on Neural Networks, 2011: 2151-2155.

[10] Zhang Y K, Hong C Y, Wang C. A real time rectangular speed limit sign recognition system[J]. CAAI Transactions on Intelligent Systems, 2010, 6: 16.

[11] 房泽平, 段建民, 郑榜贵. 基于特征颜色和SNCC的交通标志识别与跟踪[J]. 交通运输系统工程与信息, 2014, 14(1): 47-52.

    Fang Zeping, Duan Jianmin, Zheng Banggui. Traffic signs recognition and tracking based on feature color and sncc algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(1): 47-52.

[12] Jin J Q, Fu K, Zhang C S. Traffic sign recognition with hinge loss trained convolutional neural networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 1991-2000.

[13] Yin S Y, Peng O Y, Liu L B, et al. Fast traffic sign recognition with a rotation invariant binary pattern based feature[J]. Sensors, 2015, 15(1): 2161-2180.

[14] Shlens J. A tutorial on principal components analysis[J]. Arxiv, 2002, 58(3): 219-226.

[15] Liu H P, Sun F C, Yu Y L. Multitask extreme learning machine for visual tracking[J]. Cognitive Computation, 2014, 6(3): 391-404.

[16] Zhu W T, Miao J, Hu J B, et al. Vehicle detection in driving simulation using extreme learning machine[J]. Neurocomputing, 2014, 128(5): 160-165.

[17] Bartlett P L. The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network[J]. IEEE Transactions on Information Theory, 1998, 44(2): 525-536.

[18] Stallkamp J, Schlipsing M, Salmen J, et al. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition[J]. Neural Networks, 2012, 32(2): 323-332.

[19] Stallkamp J, Schlipsing M, Salmen J, et al. The German traffic sign recognition benchmark: A multi-class classification competition[C]. Proceedings of 2011 IEEE International Joint Conference on Neural Networks, 2011: 1453-1460.

[20] Ciresan D, Meier U, Masci J, et al. A committee of neural networks for traffic sign classification[C]. Proceedings of 2011 IEEE International Joint Conference on Neural Networks, 2011, 42(4): 1918-1921.

[21] Tang S S, Huang L L. Traffic sign recognition using complementary features[C]. Proceedings of 2013 IEEE Asian Conference on Pattern Recognition, 2013: 210-214.

徐岩, 韦镇余. 一种改进的交通标志图像识别算法[J]. 激光与光电子学进展, 2017, 54(2): 021001. Xu Yan, Wei Zhenyu. An Improved Traffic Sign Image Recognition Algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(2): 021001.

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