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Computer Vision Based Driver Assistance Drowsiness Detection

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Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 770))

Abstract

Nowadays, drowsiness is a serious cause of traffic accidents, a problem of major concern to society. Driver fatigue or sleepiness decreases the driver’s reaction time, reduces attention, and affects the quality of decision making which impairs the driving experience. Therefore, in this paper, a drowsiness detection system is designed based on computer vision, using a cascade of classifiers based on Haar-like features. The system is able to detect the face and eyes of the driver and determine the eyes closure or opening, which concludes the drowsiness of the driver. The paper presents the five primary steps involves which are: video acquirement, frame separation, face detection, eyes detection and drowsiness detection.

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Acknowledgements

This paper was supported in part by Fundamental Research Grant Scheme, Ministry of Higher Education, Malaysia (FRGS19-017-0625).

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Authors and Affiliations

  1. Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia

    Maryam J. S. Emashharawi, Othman O. Khalifa, Noreha Abdul Malik & Norun Farihah Abdul Malek

Authors
  1. Maryam J. S. Emashharawi
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  2. Othman O. Khalifa
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  3. Noreha Abdul Malik
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  4. Norun Farihah Abdul Malek
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Corresponding author

Correspondence to Noreha Abdul Malik .

Editor information

Editors and Affiliations

  1. Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia

    Khalid Isa

  2. Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Zainah Md. Zain

  3. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Pulau Pinang, Malaysia

    Rosmiwati Mohd-Mokhtar

  4. Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Maziyah Mat Noh

  5. Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Semarak, Kuala Lumpur, Malaysia

    Zool H. Ismail

  6. Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Ahmad Anas Yusof

  7. Faculty of Ocean Engineering Technology and Informatics (FTKKI), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia

    Ahmad Faisal Mohamad Ayob

  8. Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Bandar Seri Iskandar, Perak, Malaysia

    Syed Saad Azhar Ali

  9. Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn, Parit Raja, Johor, Malaysia

    Herdawatie Abdul Kadir

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Emashharawi, M.J.S., Khalifa, O.O., Abdul Malik, N., Abdul Malek, N.F. (2022). Computer Vision Based Driver Assistance Drowsiness Detection. In: Isa, K., et al. Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020. Lecture Notes in Electrical Engineering, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-16-2406-3_27

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