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Driver Fatigue Detection and Alert System using Non-Intrusive Eye and Yawn Detection

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Pranali Awasekar, Menaka Ravi, Shivani Doke, Zaheed Shaikh
10.5120/ijca2018917140

Pranali Awasekar, Menaka Ravi, Shivani Doke and Zaheed Shaikh. Driver Fatigue Detection and Alert System using Non-Intrusive Eye and Yawn Detection. International Journal of Computer Applications 180(44):1-5, May 2018. BibTeX

@article{10.5120/ijca2018917140,
	author = {Pranali Awasekar and Menaka Ravi and Shivani Doke and Zaheed Shaikh},
	title = {Driver Fatigue Detection and Alert System using Non-Intrusive Eye and Yawn Detection},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2018},
	volume = {180},
	number = {44},
	month = {May},
	year = {2018},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume180/number44/29438-2018917140},
	doi = {10.5120/ijca2018917140},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Driver fatigue is one the leading causes of car accidents in the world. Detecting drowsiness and alerting the driver is the easiest way to prevent mishaps. The purpose of this paper is to develop a fatigue detection and alert system. This system works by analyzing the eye closure duration and yawn frequency of the driver and alerting the driver by activating LEDs, buzzers and sending warning message to his emergency contacts. The alerts are divided into three stages of severity to take action accordingly. Facial features for determining alertness were obtained by using a camera capturing the face of the driver. The system can monitor the driver's eyes to detect early stages of sleep as well as short periods of sleep lasting 3 to 4 seconds. The application is implemented on a Raspberry Pi minicomputer with a NoIR camera, making the system economical and portable. The system not only provides an effective way to detect fatigue but also provides many forms of alerts to control the situation and compel the driver to take a break.

References

  1. Transport Accident Commission. “Fatigue Statistics.” Transport Accident Commission, TAC, 18 Oct. 2017, www.tac.vic.gov.au/road-safety/statistics/summaries/fatigue-statistics.
  2. Dobbie, K. (2002). "An Analysis of Fatigue-related Crashes on Australian Roads using an Operational Definition of Fatigue", Australian Transport Safety Bureau, Road Safety Research Report OR 23.
  3. Krajewski, J., Sommer, D., Trutschel, U., Edwards, D., & Golz, M. (2009). Steering wheel behavior based estimation of fatigue.
  4. Sahayadhas, A., Sundaraj, K., & Murugappan, M. (2012). Detecting driver drowsiness based on sensors: a review. Sensors, 12(12), 16937-16953.
  5. Ingre M., ÅKerstedt T., Peters B., Anund A., Kecklund G. Subjective sleepiness, simulated driving performance and blink duration: Examining individual differences. J. Sleep Res. 2006;15:47–53.
  6. Akin M., Kurt M., Sezgin N., Bayram M. Estimating vigilance level by using EEG and EMG signals. Neural Comput. Appl. 2008;17:227–236.
  7. Linux, and Ben Martin. “Give Your Raspberry Pi Night Vision With the PiNoir Camera.” Linux.com, 15 July 2015, www.linux.com/learn/give-your-raspberry-pi-night-vision-pinoir-camera.
  8. Deza, Elena; Deza, Michel Marie (2009). Encyclopedia of Distances. Springer. p. 94.
  9. Tiesheng Wang and Pengfei Shi, "Yawning detection for determining driver drowsiness," Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005., 2005, pp. 373-376.
  10. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154.
  11. Kazemi, V., & Josephine, S. (2014). One millisecond face alignment with an ensemble of regression trees. In 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 23 June 2014 through 28 June 2014 (pp. 1867-1874). IEEE Computer Society
  12. “S+ ResMed.” Sleep Architecture - The Shape of Your Night's Rest, sleep.mysplus.com/library/category3/article1.html.
  13. Wen-Bing Horng, Chih-Yuan Chen, Yi Chang and Chun-Hai Fan, "Driver fatigue detection based on eye tracking and dynamk, template matching," IEEE International Conference on Networking, Sensing and Control, 2004, Taipei, Taiwan, 2004, pp. 7-12.
  14. Felber, Susie. “The 4 Different Stages of Sleep.” Nokia Health, Nokia Health Blog, 17 Mar. 2015, blog.health.nokia.com/blog/2015/03/17/the-4-different-stages-of-sleep/.
  15. K. U. Anjali, A. K. Thampi, A. Vijayaraman, M. F. Francis, N. J. James and B. K. Rajan, "Real-time nonintrusive monitoring and detection of eye blinking in view of accident prevention due to drowsiness," 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, 2016, pp. 1-6.
  16. Granbery, John Hastings. "Facilitating wireless connections using a BLE beacon." U.S. Patent No. 9,648,652. 9 May 2017.

Keywords

fatigue detection, road safety, image processing, early warning, facial landmark, Haar cascade, regression trees, Bluetooth Low Energy, GPS.