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Article Navigation >  Journal of Traffic and Transportation Engineering  > 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.
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Traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier

  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Branch of Zhuhai, Guangdong Institute of Special Equipment Inspection and Research, Zhuhai 519002, Guangdong, China

  • 3.

    Dongfeng Peugeot Citroen Automobile Co., Ltd., Wuhan 430056, Hubei, China

  • 4.

    Department of Civil and Materials Engineering, University of Illinois at Chicago, Chicago 60607, Illinois, USA

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

  • Received Date: 2017-03-15
  • Publish Date: 2017-06-25
    Abstract
  • In order to improve the accuracy and real-time performance of traffic sign recognition, a traffic sign recognition method was proposed based on HOG-Gabor feature fusion and Softmax classifier. HOG (histogram of oriented gradient) feature was extracted by using the Gamma correction method, and Gabor feature was extracted by using the contrast limited adaptive histogram equalization method. According to the linear feature fusion principle, HOG and Gabor feature vectors were connected to constitute the fusional feature vector for depicting the traffic signs. Theeffectiveness of the proposed method was verified based on the GTSRB (German Traffic Sign Recognition Benchmark) data set. The recognition effects of traffic sign based on single feature and fusional feature were compared. Experimental result shows that in image enhancement, the classification accuracy based on HOG feature is 97.11% and is largest by the Gamma correction method, and the classification accuracy based on Gabor feature is 97.54% and is largest by the contrast limited adaptive histogram equalization method. The minimum classification accuracy is 97.11% by using Softmax classifier, and classification time is only 2 s. The correct recognition rate of traffic sign reaches 97.68% by using the proposed method based on HOG-Gabor fusional features, so the traffic sign recognition method based on HOG-Gabor fusional features and Softmax classifier has high recognition rate and real-time performance.

     

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