Open Access Open Access  Restricted Access Subscription or Fee Access

Driver Drowsiness Detection System Using Python, OpenCV and Raspberry Pi

Venkata Ramana Kammampati, Vyshnavi N, D.V.A.N. Ravi Kumar

Abstract


The number of accidents and deaths can be greatly decreased by using intelligent systems to prevent auto accidents. Human mistakes, such as drowsy driving, are one of the variables that significantly contribute to accidents. The system looks for symptoms of fatigue and sleepiness on a person's face while they are driving. It is based on an image processing technique. This project presents a way to analyze and anticipate driver drowsiness with the help of built-in Python and OpenCV libraries to locate eyes in the video frames. The Eye Aspect Ratio (EAR) is computed for each frame, and the outcome is then assessed against a predefined threshold value. Once tiredness is identified, a warning signal or alarm is activated to alert the driver to get up and stop being drowsy. This approach initially identifies the eyes and subsequently determines whether they are open or closed. The system detects the driver's inactivity if the eyes are closed for a minimum of 10 consecutive frames, determines that the person who drives is dozing off, and sends a warning signal or sets off an alert using the buzzer attached to the Raspberry Pi to wake the person up.


Keywords


Driver drowsiness, OpenCV, eye aspect ratio, raspberry Pi, python

Full Text:

PDF

References


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.pages-6-15, Springer Link, View at Publisher Site | Google Scholar.

G. Turan and S. Gupta, “Road accidents prevention system using drivers drowsiness detection,” International Journal of Advanced Research in Computer Engineering Technology, 2013, Issue-6, Vol-9,View at: Google Scholar https://www.irjet.net/archives/V9/i6/IRJET-V9I6122.pdf

S. Gupta and E. Garima, “Road accident prevention system using driver's drowsiness detection by combining eye closure and yawning,” International Journal of Research, Pages- 839–842, July 2014, ISSN 2348-6848, Vol-1, Issue-6, .View at: Google Scholar

M. Miranda, A. Villanueva, M. J. Buo, R. Mera bite, S. P. Perez, and J. M. Rodriguez, “Portable prevention and monitoring of driver's drowsiness focuses to eyelid movement using internet of things,” in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pages-1–5, IEEE Xplore, November 2018.View at: Google Scholar

O. Khunpisuth, T. Chotchinasri, V. Koschakosai, and N. Hnoohom, “Driver drowsiness detection using eye-closeness detection,” in November 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 661–668, IEEE Xplore, 2016.View at: Google Scholar https://ieeexplore.ieee.org/document/7907537

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. Volume 120, Issue No-6, July 2018, ISSN-2295-2310 ,View at: Google Scholar

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), Pages- 190–195, IEEE Xplore, ISSN-2189-8723 Bangkok, November 2018.View at: Google Scholar https://ieeexplore.ieee.org/document/8550026

L. B. Chen, W. J. Chang, J. P. Su et al., “A wearable-glasses-based drowsiness-fatigue-detection system for improving road safety,” in 2016 IEEE 5th Global Conference on Consumer Electronics, Pages- 12, IEEE Xplore, December 2016.View at: Google Scholar

https://ieeexplore.ieee.org/document/7800456

V. Kinage and P. Patil, “Iot based intelligent system for vehicle accident prevention and detection at real time,” in 2019 Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics, and Cloud) (I-SMAC), Pages- 409–413, Palladam, India, December 2019.View at: Google Scholar

https://ieeexplore.ieee.org/document/9032662

S. S. Kulkarni, A. D. Harale, and A. V. Thakur, “Image processing for driver's safety and vehicle control using Raspberry Pi and webcam,” in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 1288–1291, IEEE Xplore, Chennai, 2017.View at: Google Scholar https://ieeexplore.ieee.org/document/8391917


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Trends in Opto-Electro and Optical Communications