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Turnstile Jumping Detection in Real-Time Video Surveillance

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Image and Video Technology (PSIVT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11854))

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Abstract

Turnstile jumping, a common action happening on a daily basis at high volume pedestrian areas, causes various problems for society. This study proposes a novel framework in detecting tunrstile jumping with no GPU necessary. The proposed model is a combination of a YOLO v2 based human detector, a Kernelized Correlation Filters (KCF) tracker and a Motion History Image (MHI)-based Convolutional Neural Network (CNN) classifier. Experimental results show that the developed model is not only capable of operating in real-time but can also detect suspicious human actions with an accuracy rate of 91.69%

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Acknowledgement

This research is funded by Ministry of Science and Technology of Vietnam (MOST) under grant number 10/2018/DTCTKC.01.14/16-20.

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Authors and Affiliations

  1. School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam

    Huy Hoang Nguyen & Thi Nhung Ta

Authors
  1. Huy Hoang Nguyen
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  2. Thi Nhung Ta
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Corresponding author

Correspondence to Huy Hoang Nguyen .

Editor information

Editors and Affiliations

  1. Chonnam National University, Gwangju, Korea (Republic of)

    Chilwoo Lee

  2. Dalian University of Technology, Dalian, China

    Zhixun Su

  3. National Institute of Informatics, Tokyo, Japan

    Akihiro Sugimoto

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Nguyen, H.H., Ta, T.N. (2019). Turnstile Jumping Detection in Real-Time Video Surveillance. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-34879-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34878-6

  • Online ISBN: 978-3-030-34879-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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