Advertisement

Research Aspects

Chapter
  • 752 Downloads
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter presents an overview of the research aspects associated with the development of driver drowsiness detection solutions, particularly vehicle-mounted solutions that monitor the driver’s head and face for behavioral signs of potential drowsiness, such as nodding, yawning, or blinking. It summarizes relevant technologies, popular algorithms, and design challenges associated with such systems.

Keywords

Support Vector Machine Local Binary Pattern Gabor Filter Driving Simulator CMOS Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    T. Ahonen, A. Hadid, and M. Pietikäinen. Face recognition with local binary patterns. In ECCV (1), pages 469–481, 2004.Google Scholar
  2. 2.
    F. Bella. Driver perception of roadside configurations on two-lane rural roads: Effects on speed and lateral placement. Accident Analysis and Prevention, 50(0):251–262, 2013.CrossRefGoogle Scholar
  3. 3.
    L. Bergasa, J. Nuevo, M. Sotelo, R. Barea, and M. Lopez. Real-time system for monitoring driver vigilance. Intelligent Transportation Systems, IEEE Transactions on, 7(1):63–77, 2006.CrossRefGoogle Scholar
  4. 4.
    E. Cheng, B. Kong, R. Hu, and F. Zheng. Eye state detection in facial image based on linear prediction error of wavelet coefficients. In Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on, pages 1388–1392, 2009.Google Scholar
  5. 5.
    D. Dawson, C. J. van den Heuvel, K. J. Reid, S. N. Biggs, and S. D. Baulk. Chasing the silver bullet: Measuring driver fatigue using simple and complex tasks. Accident analysis and prevention, 40(1):396–402, 2008.CrossRefGoogle Scholar
  6. 6.
    W. Dong and P. Qu. Eye state classification based on multi-feature fusion. In Control and Decision Conference, 2009. CCDC ’09. Chinese, pages 231–234, 2009.Google Scholar
  7. 7.
    M. J. Flores, J. M. Armingol, and A. de la Escalera. Real-time warning system for driver drowsiness detection using visual information. Journal of Intelligent and Robotic Systems, 59(2):103–125, 2010.CrossRefGoogle Scholar
  8. 8.
    T. Hong, H. Qin, and Q. Sun. An improved real time eye state identification system in driver drowsiness detection. In Control and Automation, 2007. ICCA 2007. IEEE International Conference on, pages 1449–1453, 2007.Google Scholar
  9. 9.
    C. Jiangwei, J. Lisheng, G. Lie, G. Keyou, and W. Rongben. Driver’s eye state detecting method design based on eye geometry feature. In Intelligent Vehicles Symposium, 2004 IEEE, pages 357–362, 2004.Google Scholar
  10. 10.
    P. Konstantopoulos, P. Chapman, and D. Crundall. Driver’s visual attention as a function of driving experience and visibility. using a driving simulator to explore drivers’ eye movements in day, night and rain driving. Accident Analysis and Prevention, 42(3):827–834, 2010.Google Scholar
  11. 11.
    A. Lenskiy and J.-S. Lee. Driver’s eye blinking detection using novel color and texture segmentation algorithms. International Journal of Control, Automation and Systems, 10(2):317–327, 2012.CrossRefGoogle Scholar
  12. 12.
    Z. Li and P. Milgram. An investigation of the potential to influence braking behaviour through manipulation of optical looming cues in a simulated driving task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 49(17):1540–1544, 2005.CrossRefGoogle Scholar
  13. 13.
    C.-C. Lien and P.-R. Lin. Drowsiness recognition using the Least Correlated LBPH. In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2012 Eighth International Conference on, pages 158–161, 2012.Google Scholar
  14. 14.
    A. Liu, Z. Li, L. Wang, and Y. Zhao. A practical driver fatigue detection algorithm based on eye state. In Microelectronics and Electronics (PrimeAsia), 2010 Asia Pacific Conference on Postgraduate Research in, pages 235–238, 2010.Google Scholar
  15. 15.
    D. Liu, P. Sun, Y. Xiao, and Y. Yin. Drowsiness detection based on eyelid movement. In Education Technology and Computer Science (ETCS), 2010 Second International Workshop on, volume 2, pages 49–52, 2010.Google Scholar
  16. 16.
    Z. Liu and H. Ai. Automatic eye state recognition and closed-eye photo correction. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pages 1–4, 2008.Google Scholar
  17. 17.
    W. Nic. Program develops new test track capability. The Sensor Newsletter, 2004.Google Scholar
  18. 18.
    C. Qingzhang, W. Wenfu, and C. Yuqin. Research on eye-state based monitoring for drivers’ dozing. Intelligent Information Technology Applications, 2007 Workshop on, 1:373–376, 2009.Google Scholar
  19. 19.
    F. Rosey, S.-M. Auberlet, O. Moisan, and G. Dupré. Impact of narrower lane width: Comparison between fixed-base simulator and real data. Transportation Research Record: Journal of the Transportation Research Board, 2138(1):112–119, 2009.CrossRefGoogle Scholar
  20. 20.
    J. Telner. The Effects of Linguistic Fluency on Performance in a Simulated Cellular Telephone and Driving Situation. Canadian theses. York University (Canada), 2008.Google Scholar
  21. 21.
    J. A. Telner, D. L. Wiesenthal, and E. Bialystok. Video gamer advantages in a cellular telephone and driving task. Proceedings of the Human Factors and Ergonomic Society annual meeting, 53(23):1748–1752, 2009.CrossRefGoogle Scholar
  22. 22.
    Y.-l. Tian, T. Kanade, and J. F. Cohn. Eye-state action unit detection by Gabor Wavelets. In Proceedings of the Third International Conference on Advances in Multimodal Interfaces, ICMI ’00, pages 143–150, London, UK, UK, 2000. Springer-Verlag.Google Scholar
  23. 23.
    Z. Tian and H. Qin. Real-time driver’s eye state detection. In Vehicular Electronics and Safety, 2005. IEEE International Conference on, pages 285–289, 2005.Google Scholar
  24. 24.
    P. Viola and M. Jones. Robust real-time object detection. In International Journal of Computer Vision, 2001.Google Scholar
  25. 25.
    F. Wang, M. Zhou, and B. Zhu. A novel feature based rapid eye state detection method. In Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on, pages 1236–1240, 2009.Google Scholar
  26. 26.
    Y.-S. Wu, T.-W. Lee, Q.-Z. Wu, and H.-S. Liu. An eye state recognition method for drowsiness detection. In Vehicular Technology Conference (VTC 2010-Spring), 2010 IEEE 71st, pages 1–5, 2010.Google Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.Department of Computer & Electrical EngineeringFlorida Atlantic UniversityBoca RatonUSA
  2. 2.Department of Computer & Electrical Engineering and Computer ScienceFlorida Atlantic UniversityBoca RatonUSA
  3. 3.Department of Computer Science & EngineeringFlorida Atlantic UniversityBoca RatonUSA

Personalised recommendations