Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(266.95KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Detecting Fatigue Driving Through PERCLOS: A Review
Samuel Kim, Irfan Wisanggeni, Ryan Ros, Rania Hussein
Pages - 1 - 7     |    Revised - 31-01-2020     |    Published - 29-02-2020
Volume - 14   Issue - 1    |    Publication Date - February 29, 2020  Table of Contents
MORE INFORMATION
KEYWORDS
PERCLOS, Real-time Systems, Autonomous Driving.
ABSTRACT
In this paper, we present a literature survey about drowsy driving detection using PERCLOS metric that determines the percentage of eye closure. This metric determines that an eye is closed if the percentage of eye closure is 80% or above. When this percentage is observed for multiple frames of a video camera feed, the driver is determined to be in an unsafe fatigue status. In our research, we found that the PERCLOS metric had a 0.79 to 0.87 correlation coefficient value which exceeds the 0.7 R value needed to be considered a strong correlation coefficient. A higher value than 0.7 indicates a more linear relationship which means that the metric is dependable [1].
1 L. Tijerina, M. Gleckler, D. Stoltzfus, S. Johnston, MJ. Goodman and WW. Wierwille. (1998, Sept.) "A Preliminary Assessment of Algorithms for Drowsy and Inattentive Driver Detection on the Road," Transportation Research Board. [On-line] Available: https://ntlrepository.blob.core.windows.net/lib/17000/17900/17991/PB2001105783.pdf [Feb. 12, 2020].
2 Q. Ji, Z. Zhu, and P. Lan. (2004, Jul.) "Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue," IEEE Transactions on Vehicular Technology. [On-line] vol. 53, no. 4, pp. 1052-1068. Available: https://ieeexplore-ieee- org.offcampus.lib.washington.edu/document/1317209 [Oct. 25, 2019].
3 R. Grace, V. Byrne, D. Bierman, J.-M. Legrand, D. Gricourt, B. Davis, J. Staszewski, and B. Carnahan. (2002, Aug.) "A drowsy driver detection system for heavy vehicles," 17th DASC. AIAA/IEEE/SAE. [On-line] Digital Avionics Systems Conference. Proceedings (Cat. No.98CH36267). Available: https://ieeexplore-ieee- org.offcampus.lib.washington.edu/document/739878 [Oct. 25, 2019]
4 Liling Li, Mei Xie, Huazhi Dong. (2011, Sep.) "A method of driving fatigue detection based on eye location" 2011 IEEE 3rd International Conference on Communication Software and Networks. [On-line] Available IEEE Xplore Digital Library, https://ieeexplore-ieee org.offcampus.lib.washington.edu/document/6013949 [Oct. 25, 2019]
5 Mohsen Poursadeghiyan, Adel Mazloumi, Gebraeil Nasl Saraji, Mohammad Mehdi Baneshi, Alireza Khammar, Mohammad Hossein Ebrahimi. (2018, Sep.) "Using Image Processing in the Proposed Drowsiness Detection System Design" Available Iranian Journal of Public Health. [On-line] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174048/ [Oct. 25, 2019]
6 Cui Xu, Ying Zheng, Zengfu Wang. (2008, Aug.) "Efficient eye states detection in real-time for drowsy driving monitoring system" 2008 International Conference on Information and Automation. [On-line] Available: IEEE Xplore Digital Library, https://ieeexplore-ieee- org.offcampus.lib.washington.edu/document/4607990 [Oct. 25, 2019]
7 Lunbo Xu, Shunyang Li, Kaigui Bian, Tong Zhao, Wei Yan. (2014, Apr.) "Sober-Drive: A smartphone-assisted drowsy driving detection system" 2014 International Conference on Computing, Networking and Communications (ICNC). [On-line] Available IEEE Xplore Digital Library, https://ieeexplore-ieee-org.offcampus.lib.washington.edu/document/6785367 [Oct. 25, 2019]
8 Ilkwon Park, Jung-Ho Ahn, Hyeran Byun. (2006, Sep.) "Efficient Measurement of Eye Blinking under Various Illumination Conditions for Drowsiness Detection Systems" 18th International Conference on Pattern Recognition (ICPR'06). [On-line] Available: IEEE Xplore Digital Library, https://ieeexplore-ieee- org.offcampus.lib.washington.edu/document/1698913 [Oct. 25, 2019]
9 Cheung, Y., & Peng, Q. (2017). Method and Apparatus for Eye Gaze Tracking.
Mr. Samuel Kim
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America
samuel.yj.kim@gmail.com, syjkim75@uw.edu
Mr. Irfan Wisanggeni
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America
Mr. Ryan Ros
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America
Ms Rania Hussein
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America