Every year thousands of people die around the world in motorway accidents, and one of the main reasons for this is drivers’ drowsiness and fatigue. According to a survey by the Central Road Research Institute (CRRI) in 2019, drivers who exhaust themselves doze off, while driving are accountable for about 40% of road mishaps. To reduce the road mishaps, a system to monitor driver’s alertness by detecting the visual features of the driver by finding the drowsiness state of the driver is proposed. It deals with an algorithm which considers the frequency of the eye-blink called PERCLOS, that make use of the eye coordinates obtained from Dlib’s Haar cascade model to determine eye’s state of the driver either open or close and sounds an alarm if the driver is found to be in drowsy state, the warning can be deactivated manually rather than automatically. This algorithm performs better than current drowsiness detection systems in both accuracy as well as speed at adequate lighting conditions. The frames captured of driver are of 640*480 resolution at over 20 fps to determine drowsiness of the driver and give accuracy of 98%. It is also affordable as it does not require any expensive hardware, only a built-in Android camera is required to provide a warning sound when the proposed system predicts that the driver is drowsy. This research result can serve as an important component in ADAS, and it can ensure safety of drivers and minimise financial and personal losses caused by accidents.
A Real-Time Driver Drowsiness Detection Using OpenCV, DLib
Lect. Notes in Networks, Syst.
2022-11-06
11 pages
Article/Chapter (Book)
Electronic Resource
English
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