Abstract
This article is devoted to the actual problem of human fatigue control and attention concentration reduction in transport. The authors consider the most efficient, by their mind, method of person psycho-emotional condition assessment that is video control based on eye condition analysis. It is based on a convolutional neural network having its own topology. The problem of network optimal depth choosing to operate in real-time mode, and the problem of large accuracy indicators on a single-board ARM processor architecture computer were analyzed. As a research result, the software and hardware complex prototype was presented. This prototype allows to detect human fatigue by means of eye video image analysis. This system allows to reduce number of car accidents associated with vehicle driver falling asleep. In conclusion, the short-term project development prospects are proposed. Fatigue of the person who makes control, management or decision-making, and decrease of attention concentration on object can lead to critical consequences. The most efficient person physiological state control is video control based on eye condition analysis. The algorithm based on convolutional neural network and its hardware implementation, providing face search in the image, eye detection and analysis of eye condition by “open-closed” principle is proposed.
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References
Dushkov, B.A., et al.: Fundamentals of Engineering Psychology, p. 576, Moscow-Yekaterinburg (2002)
Alyushin, M.V., Alyushin, A.V., Belopolsky, V.M., Kolobashkina, L.V., Ushakov, V.L.: Optical technologies for monitoring systems of the current functional state of the operational composition of the management of nuclear power facilities. Global Nucl. Saf. 6, 9–77 (2003). Moscow
Melnik, O.V., Demidova, K.A., Nikiforov, M.B., Ustyukov, D.I.: Continuous monitoring of blood pressure of the vehicle crew and decision makers. Defense Technol. Sci. Tech. Collect./FSUE “NIISU” 9, 77–80 (2016)
Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953 (2012). (Basel). Publish
Ovcharenko, M.S.: Analysis and forecast of the state and level of accidents on the roads of the Russian Federation and ways to reduce it. Sci. Methodical Electron. J. Concept 15, 1661–1665 (2002)
Dimov, I.S., Derevyanko, R.E., Kotin, D.A.: Automated system for preventing the driver from falling asleep while driving. Vestn. MGTU 20(4), 659–664 (2017)
Image Processing in Aviation Vision Systems. Kostyashkin, L.N., Nikiforov, M.B. (eds.), p. 240. Fizmatlit, Moscow (2016)
Chollet, F.: Keras. https://github.com/fchollet/keras. Accessed 21 Nov 2015
Viola, P., Jones, M.: Robust Real-time Object Detection. Cambridge Research Laboratory, Cambridge (2001)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. Conf. Comput. Vis. Pattern Recogn. 1, l-511–l-518 (2001)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR, vol. 2 (2017)
Furman, G., Baharav, A., Cahan, C., Akselrod, S.: Early detection of falling asleep at the wheel: a heart rate variability approach. Comput. Cardiol. 35, 1109–1112 (2008)
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Muratov, Y.R., Nikiforov, M.B., Tarasov, A.S., Skachkov, A.M. (2020). Video-Computer Technology of Real Time Vehicle Driver Fatigue Monitoring. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_11
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