Open Access   Article Go Back

Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach

P.E. Kekong1 , I.A. Ajah2 , U. Chidiebere3

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-1 , Page no. 11-21, Jan-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i1.1121

Online published on Jan 31, 2021

Copyright © P.E. Kekong, I.A. Ajah, U. Chidiebere . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: P.E. Kekong, I.A. Ajah, U. Chidiebere, “Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.11-21, 2021.

MLA Style Citation: P.E. Kekong, I.A. Ajah, U. Chidiebere "Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach." International Journal of Computer Sciences and Engineering 9.1 (2021): 11-21.

APA Style Citation: P.E. Kekong, I.A. Ajah, U. Chidiebere, (2021). Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach. International Journal of Computer Sciences and Engineering, 9(1), 11-21.

BibTex Style Citation:
@article{Kekong_2021,
author = {P.E. Kekong, I.A. Ajah, U. Chidiebere},
title = {Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2021},
volume = {9},
Issue = {1},
month = {1},
year = {2021},
issn = {2347-2693},
pages = {11-21},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5288},
doi = {https://doi.org/10.26438/ijcse/v9i1.1121}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i1.1121}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5288
TI - Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach
T2 - International Journal of Computer Sciences and Engineering
AU - P.E. Kekong, I.A. Ajah, U. Chidiebere
PY - 2021
DA - 2021/01/31
PB - IJCSE, Indore, INDIA
SP - 11-21
IS - 1
VL - 9
SN - 2347-2693
ER -

VIEWS PDF XML
654 463 downloads 210 downloads
  
  
           

Abstract

This work presents real time drowsy driver detection and monitoring system using deep learning based behavioral approach. The aim is to design and implement software which captures live driver’s behavior during driving and train using convolutional neural network (CNN) to predict the behavior’s of the driver. This was achieved developing a drowsy driver dataset; intelligent video based device and the CNN architecture and configurations. The designs were implemented using deep learning tool and MATHLAB. The system was tested and the result showed a detection accuracy of 99.8%. MATHLAB was used to develop a prototype model of the system.

Key-Words / Index Term

Drowsy behavior, Convolutional Neural Network, Training, Deep learning, MATHLAB

References

[1]. K. “The implementation of the Nigerian Road Strategy and Traffic Crashed”; Research project National Institute for policy and strategic Studies; NIPSS Kuku; FCILT,MNI, 2018.
[2]. Premium times news paper “Five dead in Lagos-Ibadan Expressway accident”; Thursday, August, 2020.
[3]. Z. Mardi, S.N. Ashtiani, M. Mikaili; EEG-based drowsiness detection for safe driving using chaotic features and statistical tests. Journal of Medical Signals and Sensors, 1(2): 130-137, 2016.
[4]. S.M. Noori, M. Mikaeili, “Driving drowsiness detection using fusion of electroencephalography, electrooculography, and driving quality signals. J Med Signals Sensors, 6: 39-46, 2016.
[5]. H. Parker “Distracted Driver” 3 seconds is all it takes for accident to happen; 2019.
[6]. K. Li, L. Jin, Y. Jiang, H. Xian, L. Gao, (2015). Effects of driver behavior style differences and individual differences on driver sleepiness detection. Adv. Mech. Eng, doi:1687814015578354.
[7]. L. Barr, H. Howrah, Popkin S., Carrol R.J., “ A review and evaluation of emerging driver fatigue detection, John A. Volpe National Transportation Systems Center,Cambridge, Massachusetts, 2019.
[8]. J.S. Gwak, M. Shino, A. Hirao, ”Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance”. In Proceedings of the IEEE International Conference on Intelligent Transportation Systems, Maui, Hawaii, HI, USA; pp. 1794–1800, 2018.
[9]. T. Danisman, M. Bilasco, C. Djeraba, N. Ihaddadene “ Drowsy driver detection system using eye blink patterns”. Machine and Web Intelligence (ICMWI) IEEE; 230-233, 2010.
[10]. B.K. Savas, & Y. Becerikli, Real Time Driver Fatigue Detection Based on SVM Algorithm. 6th International Conference on Control Engineering & Information Technology (CEIT), 2018; doi:10.1109/ceit.8751886.
[11]. J. Rateb, A. Khalifa , K. Mohamed, A. Wael, J. Mohsen and J. Shan. “Real time driver drowsiness detection for android application using deep neural network techniques”; 9th international conference on ambient system network and technology (ANT 2018). Procedia Computer Science 00 (2018) 000–000
[12]. S. Park, F. Pan, S. Kang, C.D. Yoo “Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks”; Asian Conference on Computer Vision; Springer;154-164, 2016.
[13]. Manishi and N. Kumari ” a comprehensive study on behavioral parameters based drowsy detection techniques; international journal of computer science and engineering; Vol 8, issue 4, ISSN, 2347 – 2693. 2020.
[14]. J. Long, e. Shelhamer, T. Darrell. “Fully convolutional networks for semantic segmentation”; IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ISBN: 978-1-4673-6964-0, 2015.
[15]. F. Friedrichs, B. Yang, “Drowsiness monitoring by steering and lane data based features under real driving conditions”. In: Proceedings of the European Signal Processing Conference. Aalborg, Denmark. pp. 23–27. 2010.
[16]. L. Zhao, W. Zenkai, W. Xiaojin, Q. Liu, "Driver drowsiness detection using facial dynamic fusion information and a DBN",IET Intell. Transp. Syst., 2018, Vol. 12 Iss. 2, pp. 127-133,2017.
[17]. F. You, L. Xiaolong, G. Yunbo, H. Wang, L. Hongyi, "A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration", IEEE Access, Vol.7, 2019.
[18]. A. Kumar and R. Patra, "Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning", IEEE Journal, pp: 339-344, 2018.
[19]. M.T. Khan, H. Anwar et al., "Smart Real-Time Video Surveillance Platform for Drowsiness Detection Based on Eyelid Closure", Wireless Communications and Mobile Computing, Hindawi, Vol. 2019, pp: 1-9, 2019.
[20]. O.Ri. Karim, C. Abdelmoula and M. Masmoudi, “A Fuzzy Based Method for Driver Drowsiness Detection”, In the Proceedings of 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications", pp: 143-147, 2017.
[21]. V. Jebasheeli, R. Vadivel ” Implementation of Automated Criminal Face Detection System Using Facial Recognition Approach” International Journal of Computer Sciences and Engineering; Vol.8, Issue.9, September 2020 E-ISSN: 2347-2693
[22]. C. Ituma and T.C. Asogwa “The Application of Machine Learning For Digital Recognition Of Identical Twins To Support Global Crime Investigation” International Journal of Computer Science and Engineering (IJCSE), Issues 12; Vol 4 pp. 427–433, 2018.