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AVOID - Accident Prevention of Vehicles by Observing Instantaneous Feeds of Driver Drowsiness

by Atul Ramkrishnan, Akshay Arukandy, Niraj Gujarathi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 23
Year of Publication: 2019
Authors: Atul Ramkrishnan, Akshay Arukandy, Niraj Gujarathi
10.5120/ijca2019919067

Atul Ramkrishnan, Akshay Arukandy, Niraj Gujarathi . AVOID - Accident Prevention of Vehicles by Observing Instantaneous Feeds of Driver Drowsiness. International Journal of Computer Applications. 178, 23 ( Jun 2019), 15-21. DOI=10.5120/ijca2019919067

@article{ 10.5120/ijca2019919067,
author = { Atul Ramkrishnan, Akshay Arukandy, Niraj Gujarathi },
title = { AVOID - Accident Prevention of Vehicles by Observing Instantaneous Feeds of Driver Drowsiness },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2019 },
volume = { 178 },
number = { 23 },
month = { Jun },
year = { 2019 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number23/30674-2019919067/ },
doi = { 10.5120/ijca2019919067 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:51:13.102472+05:30
%A Atul Ramkrishnan
%A Akshay Arukandy
%A Niraj Gujarathi
%T AVOID - Accident Prevention of Vehicles by Observing Instantaneous Feeds of Driver Drowsiness
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 23
%P 15-21
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Driver fatigue is one the leading causes of car accidents in the world. The system aims to reduce the number of road accidents by detecting drowsiness and alerting the driver. The purpose of this paper is to develop a driver fatigue detection system. This system uses eye blinking frequency and yawning frequency of the driver to analyze drowsy driver and based on the level of drowsiness system will alert the driver. Driver’s facial features are captured by using a camera then this video input is used by system to monitor the driver's eyes to detect early stages of sleep as well as short periods of sleep; for video capturing system uses mobile phone camera making system portable and cost effective. Working of proposed system is based on the driver drowsiness detection method using Haar Cascade frontal face Classifier to detect the frontal facial structure followed by using dlib and Convolutional Neural Networks (CNNs) to extract information from sequence of images (video frames) for predicting driver fatigue.

References
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  7. Dlib-ml: A Machine Learning Toolkit by Davis E. King, Journal of Machine Learning Research 10 (2009) 1755-1758.
  8. http://dlib.net/imaging.html#shape_predictor - Image Processing.
  9. http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 - Image Processing.
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  11. https://magoosh.com/data-science/convolutional-neural-networks-explained/ - Convolutional Neural Networks Explained.
  12. Convolutional Neural Networks (CNN) - https://www.superdatascience.com/blogs/convolutional-neural-networks-cnn-step-1-convolution-operation
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  15. http://parnec.nuaa.edu.cn/xtan/data/datasets/dataset_B_Eye_Images.rar - Eye images dataset.
  16. http://www.site.uottawa.ca/~shervin/yawning/ - Yawning video frames dataset.
Index Terms

Computer Science
Information Sciences

Keywords

CNN dlib Driver Fatigue Detection Haar Cascade eye blinking and yawning frequency.

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