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An Automatic Monitoring System for Vehicle Drivers

Authors: Selime Ozaktas; Feyza Galip; Ibrahim Furkan Ince; Md. Haidar Sharif;

An Automatic Monitoring System for Vehicle Drivers

Abstract

{"references": ["\u201c1 in 24 report driving while drowsy\u201d, Thechart.blogs.cnn.com, 2015.", "Peters, R.D., Balwinski, S.M., Sing, H.C., Thorne, D.R., Thomas, M.L., Fox, J.E., Alicandri, E.,Wagner, E.K., \u201cEffects of partial and total sleep deprivation on driving performance\u201d, 1999.", "\u201cDrowsy driving information\u201d, Sleepdex.org, 2015.", "\u201cSleep-deprived driving\u201d, Wikipedia, 2015.", "\u201cGizmag - new and emerging technology news\u201d, Gizmag.com, 2015.", "\u201cDriver monitoring system\u201d, Wikipedia, 2015.", "\u201cToyota adds to pre-crash safety technologies\u201d, Reliableplant.com, 2015.", "Grace, R., Byrne, V., Bierman, D., Legrand, J.M., Gricourt, D., Davis, B., Staszewski, J., Carnahan, B., \u201cA drowsy driver detection system for heavy vehicles\u201d, In Proceed-ings of the 17th Digital Avionics Systems Conference, vol. 2, pp. I36/1-I36/8, 1998.", "Begum, S., \u201cIntelligent driver monitoring systems based on physiological sensor signals: A review\u201d, In 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC), pp. 282-289, 2013.", "Feng, R., Zhang, G., Cheng, B., \u201cAn on-board system for detecting driver drowsi-ness based on multi-sensor data fusion using dempster-shafer theory\u201d, In Interna-tional Conference on Networking, Sensing and Control (ICNSC'09), pp. 897-902, 2009.", "Kim, D., Han, H., Cho, S., Chong, U., \u201cDetection of drowsiness with eyes open using eeg-based power spectrum analysis\u201d, In 7th International Forum on Strategic Technology (IFOST), pp. 1-4, 2012.", "Devi, M., Choudhari, M., Bajaj, P., \u201cDriver drowsiness detection using skin color algorithm and circular hough transform\u201d, In 4th International Conference on Emerging Trends in Engineering and Technology (ICETET), pp. 129-134, 2011.", "Chen, Y., Zhang, C., Huang, X., \u201cA novel vehicle safety traffic pre-warning decision on multi-sensor information fusion\u201d, In International Conference on Convergence In-formation Technology, pp. 318-322, 2007.", "Takemura, M., Shima, T., Muramatsu, S., \u201cEmbedded image recognition systems for advanced safety vehicles\u201d, In Symposium on VLSI Circuits, pp. C146-C147, 2015.", "Tianjun, Z., Changfu, Z., \u201cA real-time rollover warning system for heavy duty vehicle\u201d, In International Conference on Challenges in Environmental Science and Comput-er Engineering (CESCE), vol. 1, pp. 324-327, 2010.", "Tianjun, Z., Changfu, Z., \u201cAn advanced methodology for rollover warning of heavy duty truck based on kalman filter state estimation\u201d, In International Confer-ence on Intelligent Computation Technology and Automation (ICICTA), vol. 1, pp. 466-469, 2010.", "\u201cOpenCV: Open Source Computer Vision\u201d, Opencv.org, 2015.", "\u201cProcessing: Open Source Programming Language for Computer Vision\u201d, Pro-cessing.org, 2015."]}

— it is a key issue to prevent life and property from the accidents caused by vehicle drivers. Alcohol can, speed, drowsy driving, and sudden heart attack are the major reasons for road accidents, which can lead to severe physical injuries, deaths, and serious economic losses. Various methods have been proposed to detect automatically those causes to prevent accidents. We have addressed an automatic system to provide protection of drivers and travelers by dint of computer vision techniques along with embedded systems and mobile communication methodologies. Sleepy modes detection and then awaken of drivers have been controlled with computer vision methods, while mobile communication and car control have been performed by embedded system components. Primary implemented method showed its potential effectiveness. If our system could be adopted in the future, the accidents related to drivers would be reduced significantly in all over the world.

Keywords

Vehicle Drivers, Drowsiness Detection, Automatic Monitoring System, Eye Tracking, Embedded Systems, Drowsiness Detection, Vehicle Drivers, Automatic Monitoring System, Eye Tracking, Embedded Systems.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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