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Hierarchical Traffic Sign Recognition

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Advances in Multimedia Information Processing - PCM 2016 (PCM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9917))

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Abstract

Traffic Sign Recognition (TSR) is very important for driverless systems and driver assistance systems. Because of the large number of the traffic sign classes and the unbalanced training data, we propose a hierarchical recognition method for traffic sign recognition. A classification tree is constructed, where the non-leaf node is constructed based on shape classification with aggregated channel features and a leaf node is constructed based on random forest classifiers with histogram of gradient for multi-class traffic sign recognition in the non-leaf node. The proposed method can overcome the inefficiency of flat classification scheme and imbalance of training data. Extensive experiments are done on three famous traffic sign datasets: the German Traffic Sign Recognition Benchmark (GTSRB), Swedish Traffic Signs Dataset (STSD), and the 2015 Traffic Sign Recognition Competition Dataset. The experimental results demonstrate the efficiency and effectiveness of our methods.

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Authors and Affiliations

  1. Computer Science Department, Xiamen University, Xiamen, China

    Yanyun Qu, Siying Yang, Weiwei Wu & Li Lin

Authors
  1. Yanyun Qu
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  2. Siying Yang
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  3. Weiwei Wu
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  4. Li Lin
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Corresponding author

Correspondence to Yanyun Qu .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Enqing Chen

  2. Jiaotong University, Xi’an, China

    Yihong Gong

  3. Zhengzhou University, Zhengzhou, China

    Yun Tie

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Qu, Y., Yang, S., Wu, W., Lin, L. (2016). Hierarchical Traffic Sign Recognition. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-48896-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48895-0

  • Online ISBN: 978-3-319-48896-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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