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A Drowsiness Detection System Based on Eye Landmarks Using IoT

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1688))

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Abstract

Drowsiness is one of the major causes of traffic accidents. This paper presents a simple and effective drowsiness detection system using IoT by locating the eyes from driving images and detecting drowsiness based on the eye aspect ratio obtained from the eye landmarks extracted. We conduct experiments under a variety of circumstances, such as adjusting the distance between the driver and the camera, wearing different accessories, and varying the driver’s head movement. Our system can detect eye landmarks well even when the driver covers most of his face with a mask and glasses or a hat and glasses.

This study is funded in part by the Can Tho University, Code: THS2020-65.

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Notes

  1. 1.

    https://www.mersec.net/115/attention_assist.html.

  2. 2.

    https://www.stopsleep.co.uk/.

  3. 3.

    http://dlib.net/.

  4. 4.

    https://www.raspberrypi.com/products/raspberry-pi-3-model-b-plus/.

  5. 5.

    https://ecadio.com/jual-modul-camera-raspberry-pi.

  6. 6.

    https://thriv.virginia.edu/researchers-find-optimal-distance-to-sit-from-steering-wheel-to-stay-safe-in-crashes/.

  7. 7.

    https://sites.google.com/view/utarldd/home.

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Author information

Authors and Affiliations

  1. Can Tho University, Can Tho, Vietnam

    Khang Nhut Lam, Vinh Phuoc Mai, Gia-Binh Quach Dang, Quoc-Bao Hong Ngo, Nhat-Hao Quan Huynh & Mai Phuc Lieu

  2. University of Colorado, Colorado Springs, USA

    Jugal Kalita

Authors
  1. Khang Nhut Lam
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  2. Vinh Phuoc Mai
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  3. Gia-Binh Quach Dang
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  4. Quoc-Bao Hong Ngo
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  5. Nhat-Hao Quan Huynh
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  6. Mai Phuc Lieu
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  7. Jugal Kalita
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Corresponding author

Correspondence to Khang Nhut Lam .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Seoul, Korea (Republic of)

    Tai M. Chung

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Lam, K.N. et al. (2022). A Drowsiness Detection System Based on Eye Landmarks Using IoT. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_52

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  • DOI: https://doi.org/10.1007/978-981-19-8069-5_52

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8068-8

  • Online ISBN: 978-981-19-8069-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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