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An Automated System for Driver Drowsiness Monitoring Using Machine Learning

  • Suvarna Nandyal
  • P. J. SushmaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

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.

Keywords

Drowsiness Facial landmarks Eye aspect ratio Mouth opening ratio Head bending values 

References

  1. 1.
    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)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Belakhdar, I., Kaaniche, W.: Detecting driver drowsiness based on single electroencephalography channel. In: International Multi Conference on Systems, Signals and Devices (2016)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on CVPR (2005)Google Scholar
  9. 9.
    Ahmad, R., Borole, J.N.: Drowsy driver identification using eye blink detection. Int. J. Comput. Sci. Inf. Technol. 6(1), 274 (2015)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Singh, S., Papanikolopoulos, N.P.: Monitoring driver fatigue using facial analysis techniques. In: IEEE Conference on Intelligent Transportation System, pp. 314–318, October 1999Google Scholar
  12. 12.
    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 2007Google Scholar
  13. 13.
    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 2004Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPoojya Doddappa Appa College of Engineering, (Affiliated to VTU Belagavi, and Approved by AICTE)KalaburagiIndia

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