Fatigue Detection Using Raspberry Pi 3
Abstract
Driver drowsiness is a primary cause of several highway calamities leads to severe physical injuries, loss of money, and loss of human life. The implementation of driver drowsiness detection in real-time will aid in avoiding major accidents. The system is designed for four-wheelers wherein the driver’s fatigue or drowsiness is detected and alerts the person. The proposed method will use 5-megapixel Raspbian camera that captures driver’s face and eyes and processes the images to detect driver’s fatigue. On the detection of drowsiness, the programmed system cautions the driver through an alarm to ensure vigilance. The proposed method constitutes of various stages to determine wakefulness of the driver. According to this output, the warning message is generated. Haar Cascade Classifiers is used to detect the blink duration of the driver and Eye Aspect Ratio (EAR) is calculated. Finally, the alert message along with car plate number is sent to the concerned person mobile with help of Ubidots cloud service and Twilio API. For this Raspberry Pi 3 with Raspbian (Linux Based) Operating System is used.
About this article
Keywords
- Driver drowsiness detection
- Raspberry Pi 3
- Raspbian camera
- OpenCV
- Feature Extraction
- Eye Aspect Ratio (EAR).
References
[1] L. Jia, D. Zhao, K. Zheng, Z. Li, G. Sun, and F. Zhang, “Smartphone-based fatigue detection system using the progressive locating method”, IET Intell. Transp. Syst, vol. 10, no. 3, pp. 148–156, 2016.
[2] J. S. Jayasenan and P. S. Smitha, “Driver Drowsiness Detection System”, vol. 4, no. 1, pp. 34–37, 2014.
[3] V. Triyanti and H. Iridiastadi, “Challenges in detecting drowsiness based on driver’s behavior”, IOP Conf. Ser. Mater. Sci. Eng, vol. 277, 2017.
[4] F. Omidi and G. N. Saraji, “Non-intrusive Methods used to Determine the Driver Drowsiness : Narrative Review Articles”, pp. 186–191, 2016.
[5] P. P. Bhatt GHPatel PG and J. A. Trivedi Patel PG, “Various Methods for Driver Drowsiness Detection : An Overview”, Int. J. Comput. Sci. Eng, vol. 9, no. 3, pp. 70–74, 2017.
[6] D. Sarkar and A. Chowdhury, “A Real-Time Embedded System Application for Driver Drowsiness and Alcoholic Intoxication Detection”, Int. J. Eng. Trends Technol, vol. 10, no. 9, pp. 461–465, 2014.
[7] R. Ahmed, Kazi Emrul Kayes Emon, and M. F. Hossain, “Robust driver fatigue recognition using image processing”, Int. Conf. Informatics, Electron. Vis, pp. 1–6, 2014.
[8] O. Khunpisuth, T. Chotchinasri, V. Koschakosai, and N. Hnoohom, “Driver Drowsiness Detection Using Eye-Closeness Detection”, 12th Int. Conf. Signal-Image Technol. Internet-Based Syst, pp. 661–668, 2016.
[9] L. F. Ibrahim et al., “Using Haar classifiers to detect driver fatigue and provide alerts”, Multimedia. Tools Appl, vol. 71, no. 3, pp. 1857–1877, 2014.
[10] T. Soukupová and J. Cech, “Real-Time Eye Blink Detection using Facial Landmarks”, 21st Comput. Vis. Winter Work, 2016.
[11] N. G. Prajapati, “Driver Drowsiness Detection with Audio-Visual Warning”, vol. 3, no. 1, pp. 294–300, 2016.
[12] A. Suganya and A. Robertson, “On-Road Drowsiness Alarm of Drivers using Raspberry Pi”, Int. J. Recent Trends Eng. Res, vol. 3, no. 11, pp. 199–204, 2017.
[13] S. Indexed and R. Agrawal, “A LOW-COST DESIGN TO DETECT”, vol. 8, no. 9, pp. 1138–1149, 2017.
[14] B. Sivakumar and K. Srilatha, “A novel method to segment blood vessels and optic disc in the fundus retinal images”, Res. J. Pharm. Biol. Chem. Sci, vol. 7, no. 3, pp. 365–373, 2016.
[15] N. L. Fitriyani, C. K. Yang, and M. Syafrudin, “Real-time eye state detection system using haar cascade classifier and circular Hough transform”, IEEE 5th Glob. Conf. Consum. Electron. GCCE, pp. 5–7, 2016.
[16] [G. J. AL-Anizy, M. J. Nordin, and M. M. Razooq, “Automatic Driver Drowsiness Detection Using Haar Algorithm and Support Vector Machine Techniques”, Asian Journal of Applied , vol. 8, no. 2. pp. 149–157, 2015.
[17] T. Azim, M. A. Jaffar, and A. M. Mirza, “Fully automated real-time fatigue detection of drivers through Fuzzy Expert Systems”, Appl. Soft Comput. J, vol. 18, pp. 25–38, 2014.
[18] A. Manuscript, “NIH Public Access”, Traffic, vol. 11, no. 2, pp. 126–136, 2010.
[19] P. Thiffault and J. Bergeron, “Monotony of road environment and driver fatigue: A simulator study”, Accid. Anal. Prev, vol. 35, no. 3, pp. 381–391, 2003.
[20] L. Li, Y. Chen, and Z. Li, “Yawning detection for monitoring driver fatigue based on two cameras”, IEEE Conf. Intell. Transp. Syst. Proceedings, pp. 12–17, 2009.
[21] R. S. Rawal and S. S. Nagtilak, “Drowsiness Detection Using RASPBERRY-Pi Model Based On Image Processing”, pp. 328–331, 2016.
[22] S. Salehian and B. Far, “Embedded Real-Time Blink Detection System for Driver Fatigue Monitoring”, Ksiresearchorg.Ipage.Com, 2015.
[23] B. Reddy, Y. H. Kim, S. Yun, C. Seo, and J. Jang, “Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks”, IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work, pp. 438–445, 2017.
[24] K. U. Anjali, A. K. Thampi, A. Vijayaraman, M. F. Francis, N. J. James, and B. K. Rajan, “Real-time nonintrusive monitoring and detection of eye blinking in view of accident prevention due to drowsiness”, Proc. IEEE Int. Conf. Circuit, 2016.
[25] M. F. Abulkhair and L. F. Ibrahim, “Using Mobile Platform to Detect and Alerts Driver Fatigue”, vol. 123, no. 8, pp. 27–35, 2015.
[26] S.V.Manikanthan and K.Baskaran “Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Network”, CiiT International Journal of Programmable Device Circuits and Systems,Print: ISSN 0974 – 973X & Online: ISSN 0974 – 9624, Issue : November 2011, PDCS112011008.
[27] T. Padmapriya, V.Saminadan, “Performance Improvement in long term Evolution-advanced network using multiple imput multiple output technique”, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9, Sp-6, pp: 990-1010, 2017.