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

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

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

	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 = {},
	doi = {10.5120/ijca2018917140},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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