Abstract
In this paper, we present a computer vision-based application with Graphical User Interface (GUI) to monitor the driver alertness level in real time. Driver alertness level is robustly achieved through the detection of the eye state (open or closed) in each frame using a Multi-Layer Perceptron (MLP), which is used to measure PERCLOS (percentage of time eyelids are close). The application is implemented with a consumer-grade computer and a webcam with passive illumination. The GUI has been developed with FLTK Library and can be easily used to configure monitoring parameters, observe in real time the result of the driver monitoring, and access to log files of previous monitoring.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
RACE. Informe 2011: La fatiga en la conducción, http://www.race.es
Sherry, P.: Fatigue countermeasures in the railroad industry: past and current developments. Association of American Railroads, Washington, D.C. (2000)
Williamson, A., Chamberlain, T.: Review of on-road driver fatigue monitoring devices. NSW Injury Risk Management Research Centre, University of New South Wales, New South Wales, Australia (2005)
Ueno, H., Kaneda, M., Tsukino, M.: Development of drowsiness detection system. In: Proceedings of the Vehicle Navigation and Information Systems Conference, pp. 15–20 (1994)
Kircher, A., Uddman, M., Sandin, J.: Vehicle control and drowsiness. Swedish National Road and Transport Research Institute, Linköping, Sweden (2002)
Dinges, D.F., Grace, R.: PERCLOS: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance. US Department of Transportation, Federal Highway Administration, Office of Motor Carrier Research and Standards, Washington, D.C., USA (1998)
Wierwille, W.W., Ellsworth, L.A., Wreggit, S.S., Fairbanks, R.J., Kirn, C.L.: Research on vehicle-based driver status/performance monitoring: development, validation, and refinement of algorithms for detection of driver drowsiness. National Highway Traffic Safety Administration, Washington, D.C., USA (1994)
Wang, Q., Yang, J., Ren, M., Zheng, Y.: Driver fatigue detection: a survey. In: Proceedings of the World Congress on Intelligent Control, pp. 8587–8591 (2006)
Seeing Machines Limited, http://www.seeingmachines.com
Ji, Q., Yang, X.: Real time eye gaze and face pose tracking for monitoring driver vigilance. Real-Time Imaging 8(5), 357–377 (2002)
Fast Light Toolkit, http://www.fltk.org
Open Computer Vision Library, http://sourceforge.net/projects/opencvlibrary
Haykin, S.: Neural networks and learning machines, 3rd edn. Prentice-Hall, London (2008)
Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
González-Ortega, D., Díaz-Pernas, F.J., Martínez-Zarzuela, M., Antón-Rodríguez, M., Perozo-Rondón, F.J. (2012). Driver Drowsiness Monitoring Application with Graphical User Interface. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2012. Lecture Notes in Computer Science, vol 7656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35377-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-35377-2_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35376-5
Online ISBN: 978-3-642-35377-2
eBook Packages: Computer ScienceComputer Science (R0)