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Traffic Sign Recognition Using Perturbation Method

  • Conference paper
Pattern Recognition (CCPR 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 484))

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Abstract

Automatic traffic sign recognition (TSR) expects high accuracy and speed for real-time applications in intelligent transportation systems. Convolutional neural networks (CNNs) have yielded state-of-the-art performance on the public dataset GTSRB, but involve intensive computation. In this paper, we propose a traffic sign recognition method using computationally efficient feature extraction and classification techniques, and using the perturbation strategy to improve the accuracy. On the GTSRB dataset, using gradient direction histogram feature and learning vector quantization (LVQ) classifier achieves a test accuracy 98.48%. Using simple perturbation operations of image translation, the accuracy is improved to 98.88%. The accuracy is higher than that of single CNN and the speed is much higher.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian, Beijing, 100044, China

    Lin-Lin Huang

  2. National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Fei Yin

Authors
  1. Lin-Lin Huang
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  2. Fei Yin
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Editor information

Editors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, 410082, Changsha, P.R. China

    Shutao Li

  2. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  3. College of electrical and information engineering, Hunan University, 410082, Changsha, P.R. China

    Yaonan Wang

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© 2014 Springer-Verlag Berlin Heidelberg

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Huang, LL., Yin, F. (2014). Traffic Sign Recognition Using Perturbation Method. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_55

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  • DOI: https://doi.org/10.1007/978-3-662-45643-9_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45642-2

  • Online ISBN: 978-3-662-45643-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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