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Driver Fatigue Detection A Survey

The system consists of modules of head-shoulder detection, face detection, eye detection, eye openness estimation, fusion, drowsiness measure percentage of eyelid closure (PERCLOS) estimation, and fatigue level classification. Internet Serv. Young, “Review of crash effectiveness of intelligent transport system,” TRaffic Accident Causation in Europe (TRACE), 2007. Our method, named SafeDrive, attempts to improve visual lane detection approaches in drastically degraded visual conditions without relying on additional active sensors. Homepage

Zheng and Z. Zhao, “A practical driver fatigue detection algorithm based on eye state,” in Proceedings of the 2nd Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia '10), pp. 235–238, Shanghai, Wang, and Y. Kawachi, S.

J. In this paper, at first causes of drowsiness are explained and then a comprehensive survey on intelligent researches for driver fatigue detection is presented. I511–I518, Cambridge, Mass, USA, December 2001. Sig.

Driver Fatigue Detection System (called FDS) has been proposed by the authors in a recent work. View at Google ScholarP. Then we try to develop an approach for detecting eye blink by analyzing the structure and color variations of human eyes. Abtahi, B.

Shi, “Yawning detection for determining driver drowsiness,” in Proceedings of the IEEE International Workshop on VLSI Design and Video Technology (IWVDVT '05), pp. 373–376, Suzhou, China, May 2005. Syst. Ergonomics 55(1), 12–22 (2012)CrossRefGoogle Scholar3.Hu, W., Hu, X., Deng, J., et al.: Mood-fatigue analyzer: towards context-aware mobile sensing applications for safe driving. WCICA 2006.

et al. (eds) Smart City 360°. J. Batista, “A drowsiness and point of attention monitoring system for driver vigilance,” in Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems (ITSC '07), pp. 702–708, Seattle, Wash, USA, Article 165Google Scholar47.Schooley, B., Hilton, B., Lee, Y., McClintock, R., Horan, T.: CrashHelp: a GIS tool for managing emergency medical responses to motor vehicle crashes.

Kriegman, and N. Comput. View at Publisher · View at Google Scholar · View at ScopusW. When drivers are in bad mood or tired, their vigilance level decreases, which may prolong the reaction time to emergency situation and lead to serious accidents.

Sci. 47(1), 115–124 (2009)CrossRefGoogle Scholar14.Fang, R., Zhao, X., Rong, J., et al.: Study on driving fatigue based on EEG signals. http://alpinedesignsmtb.com/driver-fatigue/driver-fatigue-detection-device.php Tutorials 17(3), 1557–1581 (2015)CrossRefGoogle Scholar41.Hamilton, J.: Low cost, low power servers for Internet-scale services. Regan, and K. View at Google ScholarM.

The methods of fatigue detection mainly focused on measures of the driver's state, driver performance and the combination of the driver's state and performance. McCall and M. Skip to Main Content IEEE.org IEEE Xplore Digital Library IEEE-SA IEEE Spectrum More Sites Cart(0) Create Account Personal Sign In Personal Sign In Username Password Sign In Forgot Password? a fantastic read Please enable JavaScript to use all the features on this page.Procedia Computer ScienceVolume 62, 2015, Pages 555-564open accessMobile Platform Detect and Alerts System for Driver Fatigue☆.

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http://www.jhuapl.edu/ott/technologies/technology/articles/P01471.asp 34.Samanta, B., Al-Balushi, K.R.: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features.

Hilgenstock, and R. Biometrics Compendium 21(2), 172–175 (2014)Google Scholar27.Lee, S.H., Plataniotis, K.N., et al.: Intra-class variation reduction using training expression images for sparse representation based facial expression recognition. View at Publisher · View at Google Scholar · View at ScopusT. Mozayani, and H.

J. View at Google Scholar · View at ScopusT. The first phase aimed to validate metrics that could be used to gauge operator fatigue online, to understand how the reliability of automated systems influences subjective and objective responses, and to find this Click to expose these in author workspaceb.

W. Cai and Y. Zhang, “Driver fatigue detection based on eye state analysis,” in Proceedings of the Joint Conference on Information Science, Shen Zhen, China, 2008. The simple algorithm used PERCLOS and total dwell time within the automated tasking area.

After that, we introduce some emerging platforms which designed to promote safe driving. In this paper, we propose a vision-based fatigue detection system for bus driver monitoring, which is easy and flexible for deployment in buses and large vehicles. Springer, ChamAbstractThe rapid development of the Internet of Things (IoT) has provided innovative solutions to reduce traffic accidents caused by fatigue driving.