Viplov Jiwnani, Tanmay Mathur, Sona Ameta, Vidhyanshi Khetpaliya, Charu Kavadia


This document is a complete report on the research conducted and the project made in the field of computer engineering to develop a system for driver drowsiness detection to prevent accidents from happening because of driver drowsiness. Drowsiness is one of the main causes leading to road accidents. They can be prevented by taking the effort to get enough sleep before driving or having a rest when the signs of drowsiness occur. Thus, it is not comfortable to be used in real-time driving. This project describes how to detect the eyes and mouth in a video recorded with the help of a camera. The report proposed the results and solutions to the limited implementation of the various techniques that are introduced in the project.


Road Accidents, Driver Drowsiness, Camera, Prevent Accidents.

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Mahek Jain, Bhavya Bhagirathi, Sowmyarani CN “Real-Time Driver Drowsiness Detection using Computer Vision”, IJEAT 2021.

VB Navya Kiran, Raksha R, Anisu Rahman Dr. Nagamani “Driver Drowsiness Detection “, IJERT 2020.

R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, and K. Barkaoui, “Driver drowsiness detection model using convolutional neural networks techniques for android application,” in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), IEEE, 2020.

Sukrit Mehta, Sharad Dadheech, Sahil Gumber “Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio And Eye Closure Ratio “, SUSCOM 2019.

Rizul Sharma “Driver Drowsiness system “, IJERT 2019

D. Mollicone, K. Kan, C. Mott et al., “Predicting performance and safety based on driver fatigue,” Accident Analysis Prevention, vol. 126, pp. 142–145, 2019.

S. Abraham, T. Luciya Joji, and D. Yuvaraj, “Enhancing vehicle safety with drowsiness detection and collision avoidance,” International Journal of Pure and Applied Mathematics, pages, pp. 2295–2310, 2018.

M. Y. Hossain and F. P. George, “IOT based real-time drowsy driving detection system for the prevention of road accidents,” in 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 190–195, Bangkok, 2018.

K. Saleh, M. Hossny, and S. Nahavandi, “Driving behavior classification based on sensor data fusion using lstm recurrent neural networks,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–7, IEEE, 2017.

O. Khunpisuth, T. Chotchinasri, V. Koschakosai, and N. Hnoohom, “Driver drowsiness detection using eye-closeness detection,” in 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 661–668, IEEE, 2016.

R. Malekian, A. F. Kavishe, B. T. Maharaj, P. K. Gupta, G. Singh, and H. Waschefort, “Smart vehicle navigation system using hidden Markov model and RFID technology,” Wireless Personal Communications, vol. 90, no. 4, pp. 1717–1742, 2016

A. Dasgupta, B. Kabi, A. George, S. L. Happy, and A. Routray, Cross-Domain Classification of Drowsiness in Speech: The Case of Alcohol Intoxication and Sleep Deprivation, 2015.

G. Turan and S. Gupta, “Road accidents prevention system using drivers drowsiness detection,” International Journal of Advanced Research in Computer Engineering Technology, 2013.

S. Vitabile, A. De Paola, and F. Sorbello, “A real-time non-intrusive fpga-based drowsiness detection system,” Journal of Ambient Intelligence and Humanized Computing, vol. 2, no. 4, pp. 251–262, 2011.

E. Vural, M. Cetin, A. Ercil, G. Littlewort, M. Bartlett, and J. Movellan, “Drowsy driver detection through facial movement analysis,” International Workshop on Human-Computer Interaction, vol. 4796, 2007.

DOI: https://doi.org/10.26483/ijarcs.v13i0.6856


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