Driver Drowsiness Monitoring System Using Visual Behavior And Machine Learning

被引：1
作者：
Swathi, A. [1 ]
Kumar, Ashwani [2 ]
Swathi, V. [1 ]
Sirisha, Y. [1 ]
Bhavana, D. [1 ]
Latheef, Shaik Abdul [1 ]
Abhilash, A. [1 ]
Mounika, G. [1 ]
机构：
[1] Sreyas Inst Engn & Technol, Dept CSE, Hyderabad, Telangana, India
[2] SRM Inst Sci & Technol, Fac Engn & Technol, NCR Campus, Ghaziabad, Uttar Pradesh, India
来源：
2022 5TH INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT) | 2022年
关键词：
Machine learning algorithms; EAR; MOR; Image processing techniques;
D O I：
10.1109/IMPACT55510.2022.10029275
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
One of the leading causes of car accidents is drowsy driving. The driver drowsiness detection system detects fatigue and helps to avoid accidents. Classic methods include vehicle-based, behavior-based, and physiological-based techniques. Some of these solutions, however, are inconvenient for the driver, while others necessitate the purchase of costly sensors and devices. As a result, this project demonstrates how to build low-cost, real-time driver drowsiness with long-term accuracy. In this system, drowsiness is detected using a webcam. This webcam captures the driver's frames using image processing techniques. For each frame, the eye aspect ratio (EAR) and mouth opening ratio (MOR) is calculated. The computed values and the observed threshold values are used to detect drowsiness.
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页数：4
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