Driver Drowsiness Detection

Satish, K.; Lalitesh, A.; Bhargavi, K.; Prem, Mounu; Anjali, T

doi:10.1109/iccsp48568.2020.9182237

Cited by 19 publications

(2 citation statements)

References 9 publications

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“…Almost all drivers have experienced this drowsiness problem while driving [7]. Youngsters and professional drivers are mostly affected by this drowsy driving because of continuous hours of driving without any rest.…”

Section: Proposed Approachmentioning

confidence: 99%

See 1 more Smart Citation

Driver Drowsiness Detection and Monitoring System using Machine Learning

Chinthalachervu¹,

Teja²,

Kumar³

et al. 2022

J. Phys.: Conf. Ser.

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In the present world, lots of road accidents take place due to the lack of attention and alertness of driver. This is termed as driver drowsiness. This leads to a lot of unfortunate situations causing adverse damage to human lives. The main goal of this research is the detection of driver drowsiness and an appropriate response to the detection. There are many methods which are based on the motion of the vehicle or based on the driver’s behavior. One of the methods is the physiological method which helps in distracting the driver from drowsiness and making him alert. And few methods require expensive sensors and deals with a lot of data. Therefore, this paper develops a system for detecting drowsiness in real time with proper procedure and accuracy which is acceptable. In this system, the driver’s facial expressions are captured and recorded using a webcam. Every movement in each frame is detected using few techniques of image processing. The Eye Aspect Ratio, Mouth Opening Ratio, and Nose Length Ratio are calculated using the landmark points on the face. The calculated values are compared to the threshold values developed by the system and the difference in value leads to the detection. At the same time, the machine learning algorithms are also implemented in offline manner. Based on the classification, the system has successfully achieved 95.58% of sensitivity and 100% of specificity using Support Vector Machine. This model system is compatible with all kinds of vehicles.

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Section: Proposed Approachmentioning

confidence: 99%