Real-Time Driver Alert System Using Raspberry Pi

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Jie Yi Wong
Phooi Yee Lau

Abstract


Malaysia has been ranked as one of the country in the world with deadliest road. Based on the statistic, there are around 7000 to 8000 people in the country died on the road among the population of 31 million Malaysians every year. In general, Advances Driver Assistance System (ADAS) aims to improve not only the driving experience but also consider the overall passenger safety. In recent years, driver drowsiness has been one of the major causes of road accidents, which can lead to severe physical injuries, deaths and significant economic losses. In this paper, a vison-based real-time driver alert system aimed mainly to monitor the driver’s drowsiness level and distraction level is proposed. This alert system could reduce the fatalities of car accidents by detecting driver’s face, detecting eyes region using facial landmark and calculating the rate of eyes closure in order to monitor the drowsiness level of the driver. Later, the system is embedded into the Raspberry Pi, with a Raspberry Pi camera and a speaker buzzer, and is used to alert the driver in real-time, by providing a beeping sound. Experimental results show that proposed system is practical and low-cost which could (1) embed the drowsiness detection module, and (2) provide alert notification to the driver when the driver is inattentive, using a medium loud beeping sound, in real-time.


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How to Cite
Wong, J. Y., & Lau, P. Y. (2019). Real-Time Driver Alert System Using Raspberry Pi. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 17(2), 193-203. Retrieved from https://ph02.tci-thaijo.org/index.php/ECTI-EEC/article/view/215488
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References

[1] “Global Status Report On Road Safety,” 2015. [Online] Gevena: The World Health Organization. Available: https://www.who.int/violence_injury_prevention/road_safety_status/2015/GSRRS2015_Summary_EN_final2.pdf [Accessed 26 Nov. 2017].

[2] B. Jansen, “Report: Drowsy driving is a sleeper threat in crashes,” [Online] USA TODAY. Available: https://www.usatoday.com/story/news/nation/2016/08/07/report-drowsy-drivingsleeper-threat-crashes/88300112/ [Accessed26 Nov. 2017].

[3] T. Musale and B.Pansambal, “Real Time Driver Drowsiness Detection system using Image processing,” Research in Engineering Application & Management (IJREAM), vol.2, pp.35-38, 2016.

[4] A. Eskandarian and A. Mortazavi, “Evaluation of a Smart Algorithm for Commercial Vehicle Driver Drowsiness Detection,” in 2007 IEEE Intelligent Vehicles Symposium, Istanbul, pp. 553-559, 2007.

[5] S. Vitabile, A. D. Paola and F. Sorbello, “Bright Pupil Detection in an Embedded, Real-Time Drowsiness Monitoring System,” in 2010 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, WA, 2010, pp. 661-668.

[6] C. You, N.D. Lane, F. Chen, R. Wang, Z. Chen, T.J. Bao, M. Montesde-Oca, Y. Cheng, M. Lin and L. Torresani, “Carsafe app: Alerting drowsy and distracted drivers using dual cameras on smartphones,” in Proceeding of the 11th annual international conference on Mobile systems, applications, and services, pp. 13-26, 2013.

[7] J. Wang, D. Liu, K. Zhang, X. Chen and J. Kim, “A novel real-time service architecture based on driver state detecting for improving road safety,” in 2015 IEEE International Conference on Consumer Electronics - Taiwan, Taipei, pp. 53-54, 2015.

[8] Tejasweeni Musale and B.H. Pansambal, “Driver Drowsiness Detectiontechnique Using Raspberry Pi,” International Journal of Development, Research Vol. 07, Issue, 02, pp.11499-11503, Feb
2017

[9] W. Chang, L. Chen and Y. Chiou, “Design and Implementation of a Drowsiness-FatigueDetection System Based on Wearable Smart Glasses to Increase Road Safety,” in IEEE Transactions on Consumer Electronics, vol. 64, no. 4, pp. 461-469, Nov 2018.

[10] Y. Kortli et al., “Efficient Implementation of a Real-Time Lane Departure Warning System,” in IEEE International Image Processing Applications and System Conference 2016 (IEEE IPAS2016), Hammamet, Tunisia, 5-7 Nov 2016.

[11] W. Chee, P.Y. Lau, S. Park, “Real-time Lane Keeping Assistant System on Raspberry Pi,” in IEIE Transactions on Smart Processing and Computing, vol. 6, no. 6, Dec 2017.

[12] Pascal Francis-Mezger and Vincent M. Weaver. 2018. A raspberry pi operating system for exploring advanced memory system concepts. in Proceedings of the International Symposium on Memory Systems (MEMSYS ’18). ACM, New York, NY, USA, 354-364.

[13] C. Sagonas, E. Antonakos, G. Tzimiropoulos, S. Zafeiriou and M. Pantic, “300 Faces In-The-Wild Challenge: database and results,” Image and Vision Computing, vol.47, pp 3-18, 2016.

[14] T. Soukupov´a and J. Cech, Eye-Blink Detection Using Facial Landmarks, 2016.