Use of Facial Information to Detect Driver's Sleepiness and Disorientation

  • Farhad Nesa no
  • Ali Asghar Khavasi Zanjan Branch, Islamic Azad University
Keywords: Face Detection, Detection of sleepiness, Prevention of Accident, Detection of the lack of concentration

Abstract

Road accidents in Iran account for a large volume of injuries and mortality, which have a lot of financial losses for society. Therefore, recognizing driver's sleepiness can have a great effect in reducing these injuries. One of the ways to detect fatigue and distraction is the use of surveillance systems from the driver's face. In this paper, a driver's face monitoring system is designed to estimate the driver's consciousness by extracting signs of tiredness and distraction from the eye area. These characteristics are then processed by the KNN algorithm and the RBF network in order to estimate the amount of distraction of the driver's senses. The results of the experiments on the images in the actual environment show that the proposed method has a very good accuracy. In terms of implementing the algorithm, the accuracy of the proposed system is 95.7%.

References

[1] N. L. Haworth, T. J. Triggs, and E. M. Grey, "Driver Fatigue: Concepts, Measurement and Crash Countermeasures," Human Factors Group, Department of Psychology, Monash UniversityJune 1988.
[2] Q. Ji and X. Yang, "Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance," Elsevier Real-Time Imaging, vol. 8, pp. 357–377, 2002.
[3] T. Brandt, R. Stemmer, B. Mertsching, and A. Rakotonirainy, "Affordable Visual Driver Monitoring System for Fatigue and Monotony," in IEEE International Conference on Systems, Man and Cybernetics (SMC), Hague, Netherlands, 2004, pp. 6451- 6456.
[4] P. S. Rau, "Drowsy Driver Detection and Warning System for Commercial Vehicle Drivers: Field Operational Test Design, Data Analyses and Progress," National Highway Traffic Safety Administration of USA (NHTSA)2005.
Published
2018-10-22
Section
Articles