An approach of Computer Vision Methods for Driver’s Drowsiness and Yawn Detection

Year : 2024 | Volume :01 | Issue : 01 | Page : 21-26
By

    Hrishita Jagtap

  1. Madhuri Thote

  2. Satyajeet Patil

  3. Parth Ruikar

  1. Student, Department of Computer Engineering, NBN Singhad Technical Institute Campus, Maharashtra, India
  2. Student, Department of Computer Engineering, NBN Singhad Technical Institute Campus, Maharashtra, India
  3. Student, Department of Computer Engineering, NBN Singhad Technical Institute Campus, Maharashtra, India
  4. Student, Department of Computer Engineering, NBN Singhad Technical Institute Campus, Maharashtra, India

Abstract

Numerous studies have demonstrated that 4,444 traffic crashes are primarily caused by driver drowsiness. Due to advancements in digital computer systems, tiredness behaviour may now be studied by researchers worldwide. The goal of this project is to increase road safety by preventing accidents caused by sleepy drivers. To view the driver’s face, use real-time facial recognition technology. A driver’s attentiveness and reaction time may be impacted by weariness, which raises the risk of being involved in a collision. According to an analysis of National Highway Traffic Safety Administration (NHTSA) data, between 22% and 24% of accidents are caused by sleepy driving. Compared to an awake driver, a fatigued motorist has a 4-6% higher near-miss/accident rate. The high crash rates are caused by fatigued drivers who fail to take preventative measures before colliding. An important irony in Driver Fatigue is that drivers are too exhausted to remember how much sleep they got. A lot of drivers ignore this crucial problem. The prevention of traffic accidents is greatly aided by programmes that track driver attention. The purpose of these devices is to alert drivers when they become fatigued or distracted. The duration of the driver’s eye closure is examined in this article; the driver slept off and the alarm went off. A person’s repeated yawns resemble. used the Haar cascade library and the image prediction library to programme Open CV and Python to detect yawns and facial expressions.

Keywords: Drowsiness detection, yawn, traffic, facial expression, CNN

[This article belongs to International Journal of Optical Innovations & Research(ijoir)]

How to cite this article: Hrishita Jagtap, Madhuri Thote, Satyajeet Patil, Parth Ruikar An approach of Computer Vision Methods for Driver’s Drowsiness and Yawn Detection ijoir 2024; 01:21-26
How to cite this URL: Hrishita Jagtap, Madhuri Thote, Satyajeet Patil, Parth Ruikar An approach of Computer Vision Methods for Driver’s Drowsiness and Yawn Detection ijoir 2024 {cited 2024 Mar 01};01:21-26. Available from: https://journals.stmjournals.com/ijoir/article=2024/view=133969


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Regular Issue Subscription Original Research
Volume 01
Issue 01
Received June 14, 2023
Accepted October 30, 2023
Published March 1, 2024