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LOCO: Local Context Based Faster R-CNN for Small Traffic Sign Detection

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MultiMedia Modeling (MMM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10704))

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Abstract

Given the tremendous attention on autonomous driving technology, traffic sign detection is increasingly important to guide the driving of the vehicles. However, the existed object detection methods cannot be directly employed as they always ignore the small objects. Different from general targets, the small objects only occupy a few pixels in images, which makes it hard to extract the discriminative features from them. In this paper, we propose a LOcal COntext based Faster R-CNN (LOCO) approach for traffic sign detection, which utilizes the regional proposal network for proposal generation, and local context information surrounding proposals for classifying. More importantly, a local context layer is designed to automatically extract the discriminative information from the regions around the proposal objects. The evaluations on two public real-world datasets demonstrate that our approach can significantly outperform the state-of-the-art methods.

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Notes

  1. 1.

    Please download our method in “https://github.com/CPFLAME/LOCO”.

  2. 2.

    “Tsinghua-Tencent 100K dataset,” http://cg.cs.tsinghua.edu.cn/traffic-sign.

  3. 3.

    “UISEE,” http://www.uisee.com.

  4. 4.

    “2016 CCF BDCI,” http://www.wid.org.cn.

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Acknowledgement

This work is partially supported by the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 61720106007), the National Natural Science Foundation of China (No. 61602049), the Beijing Training Project for the Leading Talents in S&T (ljrc 201502), the NSFC-Guangdong Joint Fund (U1501254), and the Fundamental Research Funds for the Central University (No. 2016RCGD32).

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Peng Cheng, Wu Liu, Yifan Zhang & Huadong Ma

Authors
  1. Peng Cheng
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  2. Wu Liu
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  3. Yifan Zhang
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  4. Huadong Ma
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Corresponding author

Correspondence to Wu Liu .

Editor information

Editors and Affiliations

  1. Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Klaus Schoeffmann

  2. Chulalongkorn University, Bangkok, Thailand

    Thanarat H. Chalidabhongse

  3. City University of Hong Kong, Hong Kong, China

    Chong Wah Ngo

  4. Chulalongkorn University, Bangkok, Thailand

    Supavadee Aramvith

  5. Dublin City University, Dublin, Ireland

    Noel E. O’Connor

  6. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  7. Tampere University of Technology, Tampere, Finland

    Moncef Gabbouj

  8. Rutgers University, Piscataway, New Jersey, USA

    Ahmed Elgammal

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Cheng, P., Liu, W., Zhang, Y., Ma, H. (2018). LOCO: Local Context Based Faster R-CNN for Small Traffic Sign Detection. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_27

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  • DOI: https://doi.org/10.1007/978-3-319-73603-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73602-0

  • Online ISBN: 978-3-319-73603-7

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