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Vehicle Detection in Hsuehshan Tunnel Using Background Subtraction and Deep Belief Network

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10192))

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Abstract

This paper proposes a method to detect vehicle in the Hsuehshan Tunnel. Vehicle detection in the Tunnel is a challenging problem due to use of heterogeneous cameras, varied camera setup locations, low resolution videos, poor tunnel illumination, and reflected lights on the tunnel wall. Furthermore, the vehicles to be detected vary greatly in shape, color, size, and appearance. The proposed method is based on background subtraction and Deep Belief Network (DBN) with three hidden layers architecture. Experimental results show that it can detect vehicles in he Tunnel effectively. The experimental accuracy rate is 96.59%.

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Acknowledgements

The authors would like to express his gratitude to Walter Slocombe and Dr. Jeffrey Lee, who assisted editing the English language for this article. This work was supported by the Ministry of Science and Technology, R.O.C., under Grants MOST 104-2221-E-845-003-.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, 202, Taiwan, ROC

    Bo-Jhen Huang & Jun-Wei Hsieh

  2. Department of Computer Science, University of Taipei, No. 1, Ai-Kuo W. Road, Taipei, 100, Taiwan, ROC

    Chun-Ming Tsai

Authors
  1. Bo-Jhen Huang
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  2. Jun-Wei Hsieh
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  3. Chun-Ming Tsai
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Corresponding author

Correspondence to Chun-Ming Tsai .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

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Huang, BJ., Hsieh, JW., Tsai, CM. (2017). Vehicle Detection in Hsuehshan Tunnel Using Background Subtraction and Deep Belief Network. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-54430-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54429-8

  • Online ISBN: 978-3-319-54430-4

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