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Driver drowsiness detection system under infrared illumination for an intelligent vehicle

Driver drowsiness detection system under infrared illumination for an intelligent vehicle

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Statistics on traffic accidents reveal that human error is the main cause of deaths and injuries on roads worldwide every day. In order to reduce the amount of such fatalities, a module for an advanced driver assistance system, which caters for automatic driver drowsiness detection and also driver distraction, is presented. Artificial intelligence algorithms are used to process the visual information in order to locate, track and analyse both the driver's face and eyes to compute the drowsiness and distraction indexes. This real-time system works during nocturnal conditions as a result of a near-infrared lighting system. Finally, examples of different driver images taken in a real vehicle at nighttime are shown to validate the proposed algorithms.

References

    1. 1)
      • Brandt, T., Stemmer, R., Mertsching, B., Rakotomirainy, A.: `Affordable visual driver monitoring system for fatigue and monotony', IEEE Int. Conf. on Systems, Man and Cybernetics, 2004, p. 6451–6456, vol. 7.
    2. 2)
      • Dong, W., Wu, X.: `Driver fatigue detection based on the distance of eyelid', IEEE Int. Workshop on VLSI Design & Video Tech., 2005, Suzhou, China.
    3. 3)
      • Friedrichs, F., Yang, B.: `Camera-based drowsiness reference for driver state classification under real driving conditions', IEEE Intelligent Vehicles Symp. (IV), 2010.
    4. 4)
    5. 5)
    6. 6)
      • Fletcher, L., Petersson, L., Zelinsky, A.: `Driver assistance systems based on vision in and out of vehicles', IEEE Proc. Intelligent Vehicle Symp., 2003, p. 322–327.
    7. 7)
      • , : `Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management', Final report DOT HS 808762, , National Highway Traffic Safety Administration, USA, 1998.
    8. 8)
      • Hagenmeyer, L.: `Development of a multimodal, universal human-machine-interface for hypovigilance-management-systems', 2007, PhD, University of Stuttgart.
    9. 9)
      • Wang, Q., Yang, J., Ren, M., Zheng, Y.: `Driver fatigue detection: a survey', IEEE Proc. Sixth World Congress on Intelligent Control, 2006, p. 8587–8591, vol. 2.
    10. 10)
    11. 11)
    12. 12)
      • Horng, W., Chen, C., Chang, Y.: `Driver fatigue detection based on eye tracking and dynamic template matching', Proc. IEEE Int. Conf. on Networking, Sensing & Control, 2004.
    13. 13)
      • Albu, B., Widsten, B., Wang, T., Lan, J., Mah, J.: `A computer vision-based system for real-time detection of sleep onset in fatigued drivers', IEEE Intelligent Vehicles Symp., 2008, p. 25–30.
    14. 14)
      • R. Grace . Drowsy driver monitor and warning system. Int. Driving Symp. on Human Factors in Driver Assessment, Training and Vehicle Design
    15. 15)
    16. 16)
    17. 17)
      • J.R. Parker . (1994) Practical computer vision using C.
    18. 18)
    19. 19)
      • N. Cristianini , J. Shawe-Taylor . (2000) An introduction to support vector machines and other kernel-based learning methods.
    20. 20)
      • I. Guyon , S. Gunn , M. Nikravesh , L. Zadeh . (2007) Feature extraction foundations and applications, vol 7 of studies inf fuzziness and soft computing.
    21. 21)
    22. 22)
      • P. Gejgus , M. Sparka . (2003) Face tracking in color video sequences.
    23. 23)
    24. 24)
      • Satake, J., Shakunaga, T.: `Multiple target tracking by appearance-based condensation tracker using structure information', Proc. 17th Int. Conf. on Patter Recognition (ICPR'04), 2004, p. 294–297, vol. 3.
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