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基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法

梁敏健 崔啸宇 宋青松 赵祥模

梁敏健, 崔啸宇, 宋青松, 赵祥模. 基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法[J]. 交通运输工程学报, 2017, 17(3): 151-158.
引用本文: 梁敏健, 崔啸宇, 宋青松, 赵祥模. 基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法[J]. 交通运输工程学报, 2017, 17(3): 151-158.
LIANG Min-jian, CUI Xiao-yu, SONG Qing-song, ZHAO Xiang-mo. Traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 151-158.
Citation: LIANG Min-jian, CUI Xiao-yu, SONG Qing-song, ZHAO Xiang-mo. Traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 151-158.

基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法

基金项目: 

国家自然科学基金项目 61201406

中央高校基本科研业务费专项资金项目 310824162022

详细信息
    作者简介:

    梁敏健(1984-), 男, 广东清远人, 长安大学工学博士研究生, 从事道路交通检测研究

    赵祥模(1966-), 男, 重庆大足人, 长安大学教授, 工学博士

  • 中图分类号: U491.52

Traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier

More Information
    Author Bio:

    LIANG Min-jian(1984-), male, doctoral student, +86-29-82334952, 185426480@qq.com

    ZHAO Xiang-mo(1966-), male, professor, PhD, +86-29-82334021, xmzhao@chd.edu.cn

  • 摘要: 为了提高交通标志识别的正确率和实时性, 提出了一种基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法。采用Gamma矫正方法提取HOG特征, 采用对比度受限的自适应直方图均衡化方法提取Gabor特征, 基于线性特征融合原理, 将提取的HOG和Gabor特征向量直接串联, 得到刻画交通标志的融合特征向量, 采用Softmax分类器对融合特征向量进行分类, 采用德国交通标志识别基准(GTSRB) 数据库测试了所提方法的有效性, 比较了基于单特征与融合特征的交通标志识别效果。试验结果表明: 在图像增强过程中, 针对HOG特征, 采用Gamma矫正方法的分类正确率最大, 为97.11%, 针对Gabor特征, 采用限制对比度的直方图均衡化方法的分类正确率最大, 为97.54%;采用Softmax分类器的最小分类正确率为97.11%, 耗时小于2s;针对HOG-Gabor融合特征, 采Softmax分类器的识别率高达97.68%, 因此, 基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法的识别率高, 实时性强。

     

  • 图  1  原始的交通标志

    Figure  1.  Raw traffic signs

    图  2  采用均值法灰度化的交通标志

    Figure  2.  Traffic signs grayed by mean method

    图  3  采用加权法灰度化的交通标志

    Figure  3.  Traffic signs grayed by weighted average method

    图  4  道路施工标志

    Figure  4.  Road construction sign

    图  5  直行标志

    Figure  5.  Go straight sign

    图  6  限速标志1

    Figure  6.  Limited speed sign 1

    图  7  路滑标志

    Figure  7.  Slippery road sign

    图  8  限速标志2

    Figure  8.  Limited speed sign 2

    图  9  限速标志3

    Figure  9.  Limited speed sign 3

    图  10  标志分类

    Figure  10.  Sign elassifications

    表  1  针对HOG特征的分类正确率

    Table  1.   Classification accuracies of HOG features

    下载: 导出CSV

    表  2  针对Gabor特征的分类正确率

    Table  2.   Classification accuracies of Gabor features

    下载: 导出CSV

    表  3  分类器性能比较

    Table  3.   Performance comparison of classifiers

    下载: 导出CSV

    表  4  分类结果比较

    Table  4.   Comparison of classification results

    下载: 导出CSV
  • [1] FLEYEH H, DAVAMI E. Eigen-based traffic sign recognition[J]. IET Intelligent Transport Systems, 2011, 5 (3): 190-196. doi: 10.1049/iet-its.2010.0159
    [2] LIM K, HONG Y, CHOI Y, et al. Real-time traffic sign recognition based on a general purpose GPU and deeplearning[J]. Plos One, 2017, 12 (3): 1-22.
    [3] ZAKLOUTA F, STANCIULESCU B. Real-time traffic sign recognition in three stages[J]. Robotics and Autonomous Systems, 2014, 62 (1): 16-24. doi: 10.1016/j.robot.2012.07.019
    [4] HUANG Zhi-yong, YU Yuan-long, GU J, et al. An efficient method for traffic sign recognition based on extreme learning machine[J]. IEEE Transactions on Cybernetics, 2017, 47 (4): 920-933. doi: 10.1109/TCYB.2016.2533424
    [5] ZENG Yu-jun, XU Xin, SHEN Da-yong, et al. Traffic sign recognition using kernel extreme learning machines with deep perceptual features[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18 (6): 1647-1653.
    [6] GREENHALGH J, MIRMEHDI M. Real-time detection and recognition of road traffic signs[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13 (4): 1498-1506. doi: 10.1109/TITS.2012.2208909
    [7] WANG Xiao-yu, HAN T X, YAN Shui-cheng. An HOG-LBP human detector with partial occlusion handling[C]∥IEEE. The2009 IEEE 12th International Conference on Computer Vision. New York: IEEE, 2009: 32-39.
    [8] 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.
    [9] WANG Gang-yi, REN Guang-hui, WU Zhi-lu, et al. A hierarchical method for traffic sign classification with support vector machines[C]∥IEEE. The 2013 International Joint Conference on Neural Networks. New York: IEEE, 2013: 1-6.
    [10] SERMANET P, LECUN Y. Traffic sign recognition with multi-scale convolutional networks[C]∥IEEE. The 2011International Joint Conference on Neural Networks. New York: IEEE, 2011: 2809-2813.
    [11] SUN Zhan-li, WANG Han, LAU W S, et al. Application of BW-ELM model on traffic sign recognition[J]. Neurocomputing, 2013, 128: 153-159.
    [12] TANG Sui-sui, HUANG Lin-lin. Traffic sign recognition using complementary features[C]∥IEEE. 2013 2nd IAPR Asian Conference on Pattern Recognition. New York: IEEE, 2013: 210-214.
    [13] ZHU Ying-ying, WANG Xing-gang, YAO Cong, et al. Traffic sign classification using two-layer image representation[C]∥IEEE. 2013 20th IEEE International Conference on Image Processing. New York: IEEE, 2013: 3755-3759.
    [14] LU Ke, DING Zheng-ming, GE S. Sparse-representationbased graph embedding for traffic sign recognition[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13 (4): 1515-1524. doi: 10.1109/TITS.2012.2220965
    [15] CIRESAN D, MEIER U, MASCI J, et al. Multi-column deep neural network for traffic sign classification[J]. Neural Networks, 2012, 32: 333-338. doi: 10.1016/j.neunet.2012.02.023
    [16] 刘占文, 赵祥模, 李强, 等. 基于图模型与卷积神经网络的交通标志识别方法[J]. 交通运输工程学报, 2016, 16 (5): 122-131. doi: 10.3969/j.issn.1671-1637.2016.05.014

    LIU Zhan-wen, ZHAO Xiang-mo, LI Qiang, et al. Traffic sign recognition method based on graphical model and convolutional neural network[J]. Journal of Traffic and Transportation Engineering, 2016, 16 (5): 122-131. (in Chinese). doi: 10.3969/j.issn.1671-1637.2016.05.014
    [17] 梁琳, 何卫平, 雷蕾, 等. 光照不均图像增强方法综述[J]. 计算机应用研究, 2010, 27 (5): 1625-1628. doi: 10.3969/j.issn.1001-3695.2010.05.006

    LIANG Lin, HE Wei-ping, LEI Lei, et al. Survey on enhancement methods for non-uniform illumination image[J]. Application Research of Computers, 2010, 27 (5): 1625-1628. (in Chinese). doi: 10.3969/j.issn.1001-3695.2010.05.006
    [18] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]∥IEEE. 2005IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2005: 886-893.
    [19] HYUN J, BAEK J, KIM J, et al. Proposing a fast circular HOG descriptor for detecting rotated objects[C]∥IEEE. The 2015 International Joint Conference on Neural Networks. New York: IEEE, 2015: 1-6.
    [20] HAGHIGHAT M, ZONOUZ S, ABDEL-MOTTALEB M. CloudID: trustworthy cloud-based and cross-enterprise biometric identification[J]. Expert Systems with Applications, 2015, 42 (21): 7905-7916. doi: 10.1016/j.eswa.2015.06.025
    [21] YAO Chang, WU Feng, CHEN Hou-jin, et al. Traffic sign recognition using HOG-SVM and grid search[C]∥IEEE. 12th International Conference on Signal Processing. New York: IEEE, 2014: 962-965.
    [22] MARTINEZ A M, KAK A C. PCA versus LDA[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23 (2): 228-233. doi: 10.1109/34.908974
    [23] MATHIAS M, TIMOFTE R, BENENSON R, et al. Traffic Sign Recognition-How far are we from the solution?[C]∥IEEE. The 2013International Joint Conference on Neural Networks. New York: IEEE, 2013: 1-8.
    [24] 宋青松, 田正鑫, 孙文磊, 等. 用于孤立数字语音识别的一种组合降维方法[J]. 西安交通大学学报, 2016, 50 (6): 42-46. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201606007.htm

    SONG Qing-song, TIAN Zheng-xin, SUN Wen-lei, et al. Combined dimension reduction method for isolated digital speech recognition[J]. Journal of Xi'an Jiaotong University, 2016, 50 (6): 42-46. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201606007.htm
    [25] STALLKAMP J, SCHLIPSING M, SALMEN J. The german traffic sign recognition benchmark: a multi-class classification competition[C]∥IEEE. The 2011 International Joint Conference on Neural Networks. New York: IEEE, 2011: 2161-4393.
    [26] CHANG C C, LIN C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2 (3): 1-27.
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出版历程
  • 收稿日期:  2017-03-15
  • 刊出日期:  2017-06-25

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