Real Time Driver Drowsiness Detection using Computer Vision
Mahek Jain1, Bhavya Bhagerathi2, Sowmyarani C N3
1Mahek Jain*, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College, Bangalore (Karnataka), India.
2Bhavya Bhagerathi, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College, Bangalore (Karnataka), India.
3Dr. Sowmyarani C N, Associate Professor, Department of Computer Science and Engineering. Bengaluru (Karnataka), India.
Manuscript received on September 22, 2021. | Revised Manuscript received on September 27, 2021. | Manuscript published on October 30, 2021. | PP: 109-113 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31591011121 | DOI: 10.35940/ijeat.A3159.1011121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including tiredness in eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.
Keywords: Drowsiness, eSpeak module, Eye aspect ratio, Yawn Detection.