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Driver's Drowsiness Detection System
Subject area: Science,Engineering and Technology · Area of research: Deep Learning, OpenCV
Abstract
The proposed system aims to lessen the number of accidents that occur due to drivers? drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including - tiredness in eyes and yawning. These features are an indication that the driver?s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value.We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.
Keywords
Drowsiness, Eye Aspect Ratio, Yawn Detection, Speak Module.
References
[1] Rahul Atul Bhope, “Computer Vision based drowsiness detection for motorized vehicles with Web Push Notifications”, IEEE 4th International Conference on Internet of Things, IEEE, Ghaziabad, India, 2019.
[2] Jasper S. Wijnands, Jason Thompson, Kerry A. Nice, Gideon D. P, Aschwanden & Mark Stevenson, “Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks”, Neural Computing and Applications, 2019.
[3] Chris Schwarz, John Gaspar, Thomas Miller & Reza Yousefian, “The detection of drowsiness using a driver monitoring system” , in Journal of Traffic Injury Prevention (Taylor and Francis Online), 2019.
[4] Aditya Ranjan, Karan Vyas, Sujay Ghadge, Siddharth Patel, Suvarna Sanjay Pawar, “Driver Drowsiness Detection System Using Computer Vision.”, in International Research Journal of Engineering and Technology (IRJET), 2020.
[5] B. Mohana, C. M. Sheela Rani, “Drowsiness Detection Based on Eye Closure and Yawning Detection”, in International Research Journal of Engineering and Technology (IRJET), 2019.
[6] National Highway Traffic Safety Administration. Drowsy Driving. Available online: https:/ www.nhtsa.gov/risky- driving/drowsy-driving (accessed on 10 May 2021).
[7] D. J. McKnight, "Method and apparatus for displaying grey-scale or color images from binary images," ed: Google Patents, 1998.
[8] T. Welsh, M. Ashikhmin, and K. Mueller, "Transferring color to greyscale images," ACM Transactions on Graphics, vol. 21, pp. 277-280, 2002.
[9] I. Garcia, S. Bronte, L. Bergasa, N. Hernandez, B. Delgado, and M. Sevillano, "Visionbased drowsiness detector for a realistic driving simulator," in Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, 2010, pp. 887-894.
[10] T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, "Drowsy driver detection system using eye blink patterns," in Machine and Web Intelligence (ICMWI), 2010 International Conference on, 2010, pp. 230-233.
[11] D. F. Dinges and R. Grace, "PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance," Federal Highway Administration. Office of motor carriers, Tech. Rep. MCRT-98-006, 1998.
[12] S. T. Lin, Y. Y. Tan, P. Y. Chua, L. K. Tey, and C. H. Ang, "PERCLOS Threshold for Drowsiness Detection during Real Driving," Journal of Vision, vol. 12, pp. 546-546, 2012.
[13] R. Grace, V. E. Byrne, D. M. Bierman, J.-M. Legrand, D. Gricourt, B. Davis, et al., "A drowsy driver detection system for heavy vehicles," in Digital Avionics Systems Conference, 1998. Proceedings., 17th DASC. The AIAA/IEEE/SAE, 1998, pp. I36/1-I36/8 vol. 2.
[14] Q. Wang, J. Yang, M. Ren, and Y. Zheng, "Driver fatigue detection: a survey," in Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, 2006, pp. 8587-8591.
[15] M. Saradadevi and P. Bajaj, "Driver fatigue detection using mouth and yawning analysis," IJCSNS International Journal of Computer Science and Network Security, vol. 8, pp. 183- 188, 2008.
[16] Bereshpolova Y, Stoelzel CR, Zhuang J, Amitai Y, Alonso JM, Swadlow HA. (2011) "Getting drowsy? Alert/nonalert transitions and visual thalamocortical network dynamics". J Neurosci. 2011, 48: 17480-17487
How to cite this paper
@article{1704211,
author = {Dhruv Pandey, Rohan Garg, Savita Sharma},
title = {Driver's Drowsiness Detection System},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {9},
pages = {303-306},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704211.pdf},
abstract = {The proposed system aims to lessen the number of accidents that occur due to drivers? drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including - tiredness in eyes and yawning. These features are an indication that the driver?s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value.We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.},
keywords = {Drowsiness, Eye Aspect Ratio, Yawn Detection, Speak Module.},
month = {March},
}