A Development of Real-Time Driver Drowsiness Detection System
Keywords:
Driver sleepiness, Driver drowsiness detection (eyes, head and mouth), Real timeAbstract
This paper proposes a development of real-time driver drowsiness detection system for reducing the chance of car accidents. The proposed system can detect and analyze the characteristics of the driver and warn the driver immediately when he is sleeping. In this paper, the system will detect the driver sleepiness from the characteristics of the eyes head and mouth, respectively via the camera module and perform their images by image processing with the Raspberry Pi 3B+ in real-time. From the experimental results in this paper, it can be confirmed that the proposed system has the potential capability to apply in the real use with the low detection response errors which are approximately 4%, 5.36% and 4.12% from eye head and mouth detector respectively.
References
K.Murthy et al., “Smart Alert System for Driver Drowsiness Using Eegand Eyelid Movements,” Mid. E. J. Sci. R., vol.14, no.5, pp.610-619, May.2013.
A. Devi et al., “Image Processing Techniques in Face Recognition,” Int. J. Com. Trends and Tech., vol.4, no. 2, pp.59-62, 2013.
P. Jaturawat et al., “Development Class Room Record System by Face Detector,” KMITL J. Inf. Tech., vol.5, no.1, pp.1-11, 2017.
X. Lu, “Image Analysis for Face Recognition,” Dept. of Computer Science and Engineering Michigan State University, East Lansing, MI, 48824, May.2003.
A. Singh et al., “Driver Drowsiness Alert System with Effective Feature Extraction,” Int. J. R. in Emer. Sci. and Tech., vol.5, no. 4, pp.26-31, 2018.
T. Soukupova et al., “Real-Time Eye Blink Detection using Facial Landmarks,” in XXI Computer Vision Winter Workshop (CVWW 2016), Rimske Toplice, Slovenia, 2016, pp. 1-8.
S. Mehta et al., “Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio,” in International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM 2019), Jaipur, India, 2019, pp. 1333-1339.
https://towardsdatascience.com/mouse-control-facial-movements-hci-app-c16b0494a971
P. Awasekar et al., “Fatigue Detection and Alert System using Non-Intrusive Eye and Yawn Detection,” Int. J. Com. App., vol.180, no.44, pp.1-5, May. 2018.
J. Feng et al., “Using Eye Aspect Ratio to Enhance Fast and Objective Assessment of Facial Paralysis,” J. Comp. and math. meth. in med., vol. 2020, pp.1-11, Jan. 2020.
R. Sutthaweekul et al., “Face Detection based-on Haar-like Features,” SWU Eng. J., vol.6, no.2, pp.34-43, 2011.
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Copyright (c) 2022 Faculty of Industrial Technology, Suan Sunandha Rajabhat University
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