Driver Fatigue State Recognition with Pixel Based Caveat Scheme Using Eye-Tracking
Driver fatigue is an important factor in the increasing
number of road accidents. Dynamic template matching method was
proposed to address the problem of real-time driver fatigue detection
system based on eye-tracking. An effective vision based approach
was used to analyze the driver’s eye state to detect fatigue. The driver
fatigue system consists of Face detection, Eye detection, Eye
tracking, and Fatigue detection. Initially frames are captured from a
color video in a car dashboard and transformed from RGB into YCbCr
color space to detect the driver’s face. Canny edge operator was used
to estimating the eye region and the locations of eyes are extracted.
The extracted eyes were considered as a template matching for eye
tracking. Edge Map Overlapping (EMO) and Edge Pixel Count
(EPC) matching function were used for eye tracking which is used to
improve the matching accuracy. The pixel of eyeball was tracked
from the eye regions which are used to determine the fatigue state of
the driver.
[1] T. C. Chieh, M. M. Mustafa, A. Hussain, E. Zahedi, B. Y. Majlis,
“Driver Fatigue Detection Using Steering Grip Force”, Proc. IEEE
Student Conference on Research and Development, Putrajaya, Malaysia,
2003, pp.45-48.
[2] K. J. Cho, B. Roy, S. Mascaro, and H. H. Asada, “A Vast DOF Robotic
Car Seat Using SMA Actuators with a Matrix Drive System,” Proc.
IEEE Robotics and Automation, New Orleans, LA, USA, Vol.4, 2004,
pp.3647- 3652.
[3] R. C. Coetzer and G. P. Hancke, “Eye Detection for a Real-Time
Vehicle Driver Fatigue Monitoring System,” Proc. 2011 IEEE
Intelligent Vehicles Symposium, Baden-Baden, Germany, 2011, pp. 66-
71.
[4] W. Dong and X. Wu, “Driver Fatigue Detection Based on the Distance
of eyelid,” Proc. IEEE VLSI Design and Video Technology, Suzhou,
China, 2005, pp. 365-368.
[5] H. Gu, Q. Ji, and Z. Zhu, “Active Facial Tracking for Fatigue
Detection", Proc. 6th IEEE Workshop on Applications of Computer
Vision, Orlando, FL, USA, 2002, pp. 137-142.
[6] H. Gu and Q. Ji, “An Automated Face Reader for Fatigue Detection,”
Proc. 6th IEEE International Conference on Automatic Face and
Gesture Recognition, Seoul, Korea, 2004, pp.111-116.
[7] W. B. Horng and C. Y. Chen, “A Real-Time Driver Fatigue Detection
System Based on Eye Tracking and Dynamic Template Matching.”
Tamkang Journal of Science and Engineering, Vol.11, No.1, 2008,
pp.65-72.
[8] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Second
Edition, Prentice Hall, Upper Saddle River, NJ, USA, 2002.
[9] T. Ito, S. Mita, K. Kozuka, T. Nakano, and S.Yamamoto, “Driver Blink
Measurement by the Motion Picture Processing and Its Application to Drowsiness Detection,” Proc. IEEE 5th International Conference on
Intelligent Transportation Systems, Singapore, 2002, pp. 168-173.
[10] Q. Ji, Z. Zhu, and P. Lan, “Real-Time Nonintrusive Monitoring and
Prediction of Driver Fatigue,” IEEE Transactions on Vehicular
Technology, Vol.53, No.4, 2004, pp.1052-1068.
[11] M. A. Recarte and L. M. Nunes, “Effects of Verbal and Spatial-Imagery
Tasks on Eye Fixations while Driving,” Journal of Experimental
Psychology: Applied, Vol.6, No.1, 2000, pp.31-43.
[12] Smart Motorist, Inc., “Driver Fatigue is an Important Cause of Road
Crashes”, http://www.smartmotorist.com/traffic-and-safety-guideline//
driver-fatigue-is-an-important-cause-of-road crashes.html.
[13] H. Wang, L. B. Zhou, and Y. Ying, “A Novel Approach for Real Time
Eye State Detection in Fatigue Awareness System,” Proc. 2010 IEEE
International Conference on Robotics Automation and Mechatronics,
2010, Singapore, pp. 528-532.
[14] J. H. Yang, Z. H. Mao, L. Tijerina, T. Pilutti, J. F. Coughlin, and E.
Feron, “Detection of Driver Fatigue Caused by Sleep Deprivation,”
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems
and Humans, Vol. 39, No. 4, 2009, pp. 694-705.
[15] K. P. Yao, W. H. Lin, C. Y. Fang, J. M. Wang, S.L. Chang, and S. W.
Chen, “Real-Time Vision-Based Driver Drowsiness/Fatigue Detection
system, Proc. IEEE 71st Vehicular Technology Conference, Taipei,
Taiwan, 2010, pp. 1-5.
[1] T. C. Chieh, M. M. Mustafa, A. Hussain, E. Zahedi, B. Y. Majlis,
“Driver Fatigue Detection Using Steering Grip Force”, Proc. IEEE
Student Conference on Research and Development, Putrajaya, Malaysia,
2003, pp.45-48.
[2] K. J. Cho, B. Roy, S. Mascaro, and H. H. Asada, “A Vast DOF Robotic
Car Seat Using SMA Actuators with a Matrix Drive System,” Proc.
IEEE Robotics and Automation, New Orleans, LA, USA, Vol.4, 2004,
pp.3647- 3652.
[3] R. C. Coetzer and G. P. Hancke, “Eye Detection for a Real-Time
Vehicle Driver Fatigue Monitoring System,” Proc. 2011 IEEE
Intelligent Vehicles Symposium, Baden-Baden, Germany, 2011, pp. 66-
71.
[4] W. Dong and X. Wu, “Driver Fatigue Detection Based on the Distance
of eyelid,” Proc. IEEE VLSI Design and Video Technology, Suzhou,
China, 2005, pp. 365-368.
[5] H. Gu, Q. Ji, and Z. Zhu, “Active Facial Tracking for Fatigue
Detection", Proc. 6th IEEE Workshop on Applications of Computer
Vision, Orlando, FL, USA, 2002, pp. 137-142.
[6] H. Gu and Q. Ji, “An Automated Face Reader for Fatigue Detection,”
Proc. 6th IEEE International Conference on Automatic Face and
Gesture Recognition, Seoul, Korea, 2004, pp.111-116.
[7] W. B. Horng and C. Y. Chen, “A Real-Time Driver Fatigue Detection
System Based on Eye Tracking and Dynamic Template Matching.”
Tamkang Journal of Science and Engineering, Vol.11, No.1, 2008,
pp.65-72.
[8] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Second
Edition, Prentice Hall, Upper Saddle River, NJ, USA, 2002.
[9] T. Ito, S. Mita, K. Kozuka, T. Nakano, and S.Yamamoto, “Driver Blink
Measurement by the Motion Picture Processing and Its Application to Drowsiness Detection,” Proc. IEEE 5th International Conference on
Intelligent Transportation Systems, Singapore, 2002, pp. 168-173.
[10] Q. Ji, Z. Zhu, and P. Lan, “Real-Time Nonintrusive Monitoring and
Prediction of Driver Fatigue,” IEEE Transactions on Vehicular
Technology, Vol.53, No.4, 2004, pp.1052-1068.
[11] M. A. Recarte and L. M. Nunes, “Effects of Verbal and Spatial-Imagery
Tasks on Eye Fixations while Driving,” Journal of Experimental
Psychology: Applied, Vol.6, No.1, 2000, pp.31-43.
[12] Smart Motorist, Inc., “Driver Fatigue is an Important Cause of Road
Crashes”, http://www.smartmotorist.com/traffic-and-safety-guideline//
driver-fatigue-is-an-important-cause-of-road crashes.html.
[13] H. Wang, L. B. Zhou, and Y. Ying, “A Novel Approach for Real Time
Eye State Detection in Fatigue Awareness System,” Proc. 2010 IEEE
International Conference on Robotics Automation and Mechatronics,
2010, Singapore, pp. 528-532.
[14] J. H. Yang, Z. H. Mao, L. Tijerina, T. Pilutti, J. F. Coughlin, and E.
Feron, “Detection of Driver Fatigue Caused by Sleep Deprivation,”
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems
and Humans, Vol. 39, No. 4, 2009, pp. 694-705.
[15] K. P. Yao, W. H. Lin, C. Y. Fang, J. M. Wang, S.L. Chang, and S. W.
Chen, “Real-Time Vision-Based Driver Drowsiness/Fatigue Detection
system, Proc. IEEE 71st Vehicular Technology Conference, Taipei,
Taiwan, 2010, pp. 1-5.
@article{"International Journal of Information, Control and Computer Sciences:70151", author = "K. Thulasimani and K. G. Srinivasagan", title = "Driver Fatigue State Recognition with Pixel Based Caveat Scheme Using Eye-Tracking", abstract = "Driver fatigue is an important factor in the increasing
number of road accidents. Dynamic template matching method was
proposed to address the problem of real-time driver fatigue detection
system based on eye-tracking. An effective vision based approach
was used to analyze the driver’s eye state to detect fatigue. The driver
fatigue system consists of Face detection, Eye detection, Eye
tracking, and Fatigue detection. Initially frames are captured from a
color video in a car dashboard and transformed from RGB into YCbCr
color space to detect the driver’s face. Canny edge operator was used
to estimating the eye region and the locations of eyes are extracted.
The extracted eyes were considered as a template matching for eye
tracking. Edge Map Overlapping (EMO) and Edge Pixel Count
(EPC) matching function were used for eye tracking which is used to
improve the matching accuracy. The pixel of eyeball was tracked
from the eye regions which are used to determine the fatigue state of
the driver.", keywords = "Driver fatigue detection, Driving safety, Eye
tracking, Intelligent transportation system, Template matching.", volume = "8", number = "11", pages = "2089-6", }
