Abstract
Driver fatigue due to continuous work or improper sleep is one among the foremost reasons for lethal road accidents across the globe. In order to avoid accidents, driver condition needs to be monitored and an alert signal should be given to avoid any accidents. The existing solutions to detect drowsiness involve expensive sensors and costly devices. In this work, a real time, light weight and less costly framework and implementation for detecting driver’s drowsiness is proposed. This paper proposes an effective drowsiness detection system for driver using eye movement and yawning detection. The collective use of mouth and eye condition detection, i.e., if yawning is detected with eyes closed, fetches better information about the detection of fatigue or drowsiness in driver. In order to evade any critical situation, the proposed work will give a sound alert signal to alert the driver and suggest switching the transmission mode from manual to autopilot. The system detects doziness of driver by detecting yawning and closing of eyes as a precautionary measure by using face images captured through camera. The experimental result shows that the suggested approach being real time and lightweight also performs well.
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References
Wierwille WW (1995) Overview of research on driver drowsiness definition and driver drowsiness detection. In: Proceedings of the international technical conference on the enhanced safety of vehicles, vol 1995. National Highway Traffic Safety Administration
Honda Homepage, http://www.owners.honda.com, Driver Attention Monitor. Last accessed 23 Mar 2018
Rau PS (2005) Drowsy driver detection and warning system for commercial vehicle drivers: field operational test design, data analyses, and progress. In: 19th international conference on enhanced safety of vehicles, pp 6–9
Emami S, Suciu VP (2012) Facial recognition using OpenCV. J Mobile Embed Distrib Syst 4(1):38–43
Rajput MV, Bakal JW (2013) Execution scheme for driver drowsiness detection using yawning feature. Int J Comput Appl 62(6)
Abtahi S, Hariri B, Shirmohammadi S (2011) Driver drowsiness monitoring based on yawning detection. In: 2011 IEEE international instrumentation and measurement technology conference, pp 1–4
Khan M, Chakraborty S, Astya R, Khepra S (2019) Face detection and recognition using OpenCV. In: International conference on computing, communication, and intelligent systems
Maior CBS, Moura MC, de Santana JM, do Nascimento LM, Macedo JB, Lins ID, Droguett EL (2018) Real-time SVM classification for drowsiness detection using eye aspect ratio. Probab Saf Assess Manag PSAM 14(09)
Chung JJ, Kim HJ (2020) An automobile environment detection system based on deep neural network and its implementation using IoT-enabled in-vehicle air quality sensors. Sustainability 12(6)
Padilla R, Costa Filho CFF, Costa MGF (2012) Evaluation of haar cascade classifiers designed for face detection. World Acad Sci Eng Technol 64:362–365
Jang SW, Ahn B (2020) Implementation of detection system for drowsy driving prevention using image recognition and IoT. Sustainability 12(7):3037
Al-Mimi H, Al-Dahoud A, Fezari M, Daoud MS (2020) A study on new arduino NANO board for WSN and IoT applications. Int J Adv Sci Technol 29(4):10223–10230
Viarbitskaya T, Dobrucki A (2018) Audio processing with using Python language science libraries. In: Signal processing: algorithms, architectures, arrangements, and applications, pp 350–354
Oxer J, Blemings H (2011) Practical arduino: cool projects for open source hardware. Apress
Jo J, Lee SJ, Jung HG, Park KR, Kim J (2011) Vision-based method for detecting driver drowsiness and distraction in driver monitoring system. Opt Eng 50(12)
Cech J, Soukupova T (2016) Real-time eye blink detection using facial landmarks. Cent Mach Perception Dep Cybern Fac Electr Eng Czech Tech Univ Prague:1–8
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Chawla, I., Purwar, A., Agarwal, S., Agrawal, S., Ahlawat, R. (2023). An Automatic Early Alert System on Detecting Dozing Driver. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_12
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DOI: https://doi.org/10.1007/978-981-99-5997-6_12
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