Fatigue Detection using Artificial Intelligence to Prevent Accident

Authors

  • S. Kiruthiga
  • S. Sharukhan
  • R. Mugunthan
  • B. Mukesh Kumar

DOI:

https://doi.org/10.37591/joipprp.v7i2.2461

Abstract

Each year many people lose their lives due to road accidents around the world. Major accidents are due to drowsy driving. Fatigue and micro sleep of the driver are often the root cause of serious accidents. However, initial signs of tiredness can be detected before a critical situation arises. The method used to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors. Therefore, in this paper, light-weight, real-time driver’s drowsiness detection system is developed and implemented on raspberry pi. The system capture the live video and detects driver’s face in every frame by employing image processing techniques. The system is able to detect facial landmarks, computes Eye Aspect Ratio (EAR) and Eye Closure Ratio (ECR) to detect driver’s drowsiness based on adaptive thresholding. Machine learning algorithms have been used for better approach.

Keywords: Drowsiness detection, Facial Landmark Detection, EAR (Eye Aspect Ratio), ECR (Eye Closure Ratio), Fatigue Detection, Non-Intrusive Methods, Driver monitoring system.

Cite this Article: S.Kiruthiga, S. Sharukhan, R. Mugunthan, B. Mukesh Kumar. Fatigue Detection using Artificial Intelligence to Prevent Accident. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 1–6p.

Downloads

Published

2020-08-12

Issue

Vol. 7 No. 2 (2020)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.