Driver Drowsiness Detection Using Eye-Closeness Detection

Oraan Khunpisuth; Taweechai Chotchinasri; Varakorn Koschakosai; Narit Hnoohom

doi:10.1109/SITIS.2016.110

Driver Drowsiness Detection Using Eye-Closeness Detection

Oraan Khunpisuth
, Taweechai Chotchinasri
, Varakorn Koschakosai
, Narit Hnoohom

Department of Computer Engineering

Mahidol University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

88 Citations (Scopus)

Abstract

The purpose of this paper was to devise a way to alert drowsy drivers in the act of driving. One of the causes of car accidents comes from drowsiness of the driver. Therefore, this study attempted to address the issue by creating an experiment in order to calculate the level of drowsiness. A requirement for this paper was the utilisation of a Raspberry Pi Camera and Raspberry Pi 3 module, which were able to calculate the level of drowsiness in drivers. The frequency of head tilting and blinking of the eyes was used to determine whether or not a driver felt drowsy. With an evaluation on ten volunteers, the accuracy of face and eye detection was up to 99.59 percent.

Original language	English
Title of host publication	Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
Editors	Giuseppe De Pietro, Albert Dipanda, Richard Chbeir, Luigi Gallo, Kokou Yetongnon
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	661-668
Number of pages	8
ISBN (Electronic)	9781509056989
DOIs	https://doi.org/10.1109/SITIS.2016.110
Publication status	Published - 21 Apr 2017
Event	12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016 - Naples, Italy Duration: 28 Nov 2016 → 1 Dec 2016

Publication series

Name	Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016

Conference

Conference	12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
Country/Territory	Italy
City	Naples
Period	28/11/16 → 1/12/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Drowsiness
Eye Detection
Face Detection
Geometric Rotation
Region of Interest

Access to Document

10.1109/SITIS.2016.110

Cite this

Khunpisuth, O., Chotchinasri, T., Koschakosai, V., & Hnoohom, N. (2017). Driver Drowsiness Detection Using Eye-Closeness Detection. In G. De Pietro, A. Dipanda, R. Chbeir, L. Gallo, & K. Yetongnon (Eds.), Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016 (pp. 661-668). Article 7907537 (Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SITIS.2016.110

Khunpisuth, Oraan ; Chotchinasri, Taweechai ; Koschakosai, Varakorn et al. / Driver Drowsiness Detection Using Eye-Closeness Detection. Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016. editor / Giuseppe De Pietro ; Albert Dipanda ; Richard Chbeir ; Luigi Gallo ; Kokou Yetongnon. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 661-668 (Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016).

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title = "Driver Drowsiness Detection Using Eye-Closeness Detection",

abstract = "The purpose of this paper was to devise a way to alert drowsy drivers in the act of driving. One of the causes of car accidents comes from drowsiness of the driver. Therefore, this study attempted to address the issue by creating an experiment in order to calculate the level of drowsiness. A requirement for this paper was the utilisation of a Raspberry Pi Camera and Raspberry Pi 3 module, which were able to calculate the level of drowsiness in drivers. The frequency of head tilting and blinking of the eyes was used to determine whether or not a driver felt drowsy. With an evaluation on ten volunteers, the accuracy of face and eye detection was up to 99.59 percent.",

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author = "Oraan Khunpisuth and Taweechai Chotchinasri and Varakorn Koschakosai and Narit Hnoohom",

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Khunpisuth, O, Chotchinasri, T, Koschakosai, V & Hnoohom, N 2017, Driver Drowsiness Detection Using Eye-Closeness Detection. in G De Pietro, A Dipanda, R Chbeir, L Gallo & K Yetongnon (eds), Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016., 7907537, Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 661-668, 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, Naples, Italy, 28/11/16. https://doi.org/10.1109/SITIS.2016.110

Driver Drowsiness Detection Using Eye-Closeness Detection. / Khunpisuth, Oraan; Chotchinasri, Taweechai; Koschakosai, Varakorn et al.
Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016. ed. / Giuseppe De Pietro; Albert Dipanda; Richard Chbeir; Luigi Gallo; Kokou Yetongnon. Institute of Electrical and Electronics Engineers Inc., 2017. p. 661-668 7907537 (Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Driver Drowsiness Detection Using Eye-Closeness Detection

AU - Khunpisuth, Oraan

AU - Chotchinasri, Taweechai

AU - Koschakosai, Varakorn

AU - Hnoohom, Narit

PY - 2017/4/21

Y1 - 2017/4/21

N2 - The purpose of this paper was to devise a way to alert drowsy drivers in the act of driving. One of the causes of car accidents comes from drowsiness of the driver. Therefore, this study attempted to address the issue by creating an experiment in order to calculate the level of drowsiness. A requirement for this paper was the utilisation of a Raspberry Pi Camera and Raspberry Pi 3 module, which were able to calculate the level of drowsiness in drivers. The frequency of head tilting and blinking of the eyes was used to determine whether or not a driver felt drowsy. With an evaluation on ten volunteers, the accuracy of face and eye detection was up to 99.59 percent.

AB - The purpose of this paper was to devise a way to alert drowsy drivers in the act of driving. One of the causes of car accidents comes from drowsiness of the driver. Therefore, this study attempted to address the issue by creating an experiment in order to calculate the level of drowsiness. A requirement for this paper was the utilisation of a Raspberry Pi Camera and Raspberry Pi 3 module, which were able to calculate the level of drowsiness in drivers. The frequency of head tilting and blinking of the eyes was used to determine whether or not a driver felt drowsy. With an evaluation on ten volunteers, the accuracy of face and eye detection was up to 99.59 percent.

KW - Drowsiness

KW - Eye Detection

KW - Face Detection

KW - Geometric Rotation

KW - Region of Interest

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M3 - Conference contribution

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BT - Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016

A2 - De Pietro, Giuseppe

A2 - Dipanda, Albert

A2 - Chbeir, Richard

A2 - Gallo, Luigi

A2 - Yetongnon, Kokou

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016

Y2 - 28 November 2016 through 1 December 2016

ER -

Khunpisuth O, Chotchinasri T, Koschakosai V, Hnoohom N. Driver Drowsiness Detection Using Eye-Closeness Detection. In De Pietro G, Dipanda A, Chbeir R, Gallo L, Yetongnon K, editors, Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 661-668. 7907537. (Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016). doi: 10.1109/SITIS.2016.110

Driver Drowsiness Detection Using Eye-Closeness Detection

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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