Abstract
Now a days the road accidents are increasing, the primary cause for these accidents is the drowsy driving which leads to death. This is the reason that the detection of driver drowsiness and its indication is foremost in real world. Most of the methods used are vehicle based or Physiological based. Some methods are intrusive and distract the driver, some require expensive sensors manually. But in today’s era real time driver’s drowsiness detection system is very much essential. Hence, the proposed system is developed. In this work a webcam records the video where the driver’s face is detected. Facial landmarks are pointed on the recognized face and the Mouth Opening Ratio(MOR), Eye Aspect Ratio (EAR), and head bending values are calculated, subjective to these values drowsiness is detected based on threshold, and an alarm is given. As the drowsiness is the stage where the driver is unmindful of persons walking on the road, so the pedestrian is detected to avoid any calamity and potholes are identified to avoid the sudden changes in the driving speed which is caused by drowsiness. From the observation, it is found that proposed system works well with 98% of accuracy. If the user is slightly bend for head and mouth then accuracy is less.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abas, A., Mellor, J., Chen, X.: Non-intrusive drowsiness detection by employing Support Vector Machine. In: 2014 20th International Conference on Automation and Computing (ICAC), Bedfordshire, UK, pp. 188–193 (2014)
Picot, A., Charbonnier, S.: On-line detection of drowsiness using brain and visual information. IEEE Trans. Syst. Man Cybern.—Part A: Syst. Hum. 42(3), 764–775 (2012)
Sengupta, A., Dasgupta, A., Chaudhuri, A., George, A., Routray, A., Guha, R.: A multimodal system for assessing alertness levels due to cognitive loading. IEEE Trans. Neural Syst. Rehabil. Eng. 25(7), 1037–1046 (2017)
Alshaqaqi, B., Baquhaizel, A.S., Ouis, M.E.A., Bouumehed, M., Ouamri, A., Keche, M.: Driver drowsiness detection system. In: IEEE International Workshop on Systems, Signal Processing and Their Applications (2013)
Bhowmick, B., Chidanand, K.S.: Detection and classification of eye state in IR camera for driver drowsiness detection. In: IEEE International Conference on Signal and Image Processing Applications (2009)
Belakhdar, I., Kaaniche, W.: Detecting driver drowsiness based on single electroencephalography channel. In: International Multi Conference on Systems, Signals and Devices (2016)
Chui, K.T., Tsang, K.F., Chi, H.R., Ling, B.W.K., Wu, C.K.: An accurate ECG based transportation safety drowsiness detection scheme. IEEE Trans. Ind. Inform. 12(4), 1438–1452 (2016)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on CVPR (2005)
Ahmad, R., Borole, J.N.: Drowsy driver identification using eye blink detection. Int. J. Comput. Sci. Inf. Technol. 6(1), 274 (2015)
Vitabile, S., De Paola, A.: Bright pupil detection in an embedded, real-time drowsiness monitoring system. In: 24th IEEE International Conference on Advanced Information Networking and Applications (2010)
Singh, S., Papanikolopoulos, N.P.: Monitoring driver fatigue using facial analysis techniques. In: IEEE Conference on Intelligent Transportation System, pp. 314–318, October 1999
Hong, T., Qin, H.: An improved real time eye state identification system in driver drowsiness detection. In: IEEE International Conference on Control and Automation, Guangzhou, China, 30 May–1 June 2007
Horng, W.B., Chen, C.Y., Chang, Y., Fan, C.H.: Driver fatigue detection based on eye tracking and dynamic template matching. In: IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, 21–23 March 2004
Ou, W.L., Shih, M.H., Chang, C.W., Yu, X.H., Fan, C.P.: Intelligent video-based drowsy driver detection system under various illuminations and embedded software implementation. In: 2015 International Conference on Consumer Electronics, Taiwan (2015)
Tian, Z., Qin, H.: Real-time driver’s eye state detection. In: IEEE International Conference on Vehicular Electronics and Safety, 14–16 October 2005, pp. 285–289 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nandyal, S., Sushma, P.J. (2020). An Automated System for Driver Drowsiness Monitoring Using Machine Learning. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-34080-3_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34079-7
Online ISBN: 978-3-030-34080-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)