Visual Monitoring of Driver Inattention

  • Luis M. Bergasa
  • Jesús Nuevo
  • Miguel A. Sotelo
  • Rafael Barea
  • Elena Lopez
Part of the Studies in Computational Intelligence book series (SCI, volume 132)

The increasing number of traffic accidents due to driver inattention has become a serious problem for society. Every year, about 45,000 people die and 1.5 million people are injured in traffic accidents in Europe. These figures imply that one person out of every 200 European citizens is injured in a traffic accident every year and that around one out 80 European citizens dies 40 years short of the life expectancy. It is known that the great majority of road accidents (about 90–95%) are caused by human error. More recent data has identified inattention (including distraction and falling asleep at the wheel) as the primary cause of accidents, accounting for at least 25% of the crashes [15]. Road safety is thus a major European health problem. In the “White Paper on European Transport Policy for 2010,” the European Commission declares the ambitious objective of reducing by 50% the number of fatal accidents on European roads by 2010 (European Commission, 2001).

This chapter presents an original system for monitoring driver inattention and alerting the driver when he is not paying adequate attention to the road in order to prevent accidents. According to [40] the driver inattention status can be divided into two main categories: distraction detection and identifying sleepiness. Likewise, distraction can be divided in two main types: visual and cognitive. Visual distraction is straightforward, occurring when drivers look away from the roadway (e.g., to adjust a radio). Cognitive distraction occurs when drivers think about something not directly related to the current vehicle control task (e.g., conversing on a hands-free cell phone or route planning). Cognitive distraction impairs the ability of drivers to detect targets across the entire visual scene and causes gaze to be concentrated in the center of the driving scene. This work is focused in the sleepiness category. However, sleepiness and cognitive distraction partially overlap since the context awareness of the driver is related to both, which represent mental occurrences in humans [26].


Finite State Machine Active Appearance Model Visual Distraction Visual Monitoring Face Orientation 
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.


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  1. 1.
    Inc. Agilent Technologies. Application Note 1118: Compliance of Infrared Communication Products to IEC 825-1 and CENELEC EN 60825-1, 1999.Google Scholar
  2. 2.
    J.M. Alonso, S. Guillaume, and L. Magdalena. KBCT, knowledge base control tool, 2003. URL
  3. 3.
    Anon. Perclos and eyetracking: Challenge and opportunity. Technical report, Applied Science Laboratories, Bedford, MA, 1999. URL
  4. 4.
    S. Baker and I. Matthews. Lucas-Kanade 20 years on: A unifying framework. International Journal of Computer Vision, 56(3):221–255, March 2004.CrossRefGoogle Scholar
  5. 5.
    L.M. Bergasa, J. Nuevo, M.A. Sotelo, R. Barea, and M.E. Lopez. Real-time system for monitoring driver vigilance. Intelligent Transportation Systems, IEEE Transactions on Intelligent Transportation Systems, 7(1):63–77, 2006.CrossRefGoogle Scholar
  6. 6.
    S. Boverie, J.M. Leqellec, and A. Hirl. Intelligent systems for video monitoring of vehicle cockpit. In International Congress and Exposition ITS. Advanced Controls and Vehicle Navigation Systems, pp. 1–5, 1998.Google Scholar
  7. 7.
    G. Bradski, A. Kaehler, and V. Pisarevsky. Learning-based computer vision with intel’s open source computer vision library. Intel Technology Journal, 09(02), May 2005.Google Scholar
  8. 8.
    P. Campadelli, R. Lanzarotti, and G. Lipori. Eye localization: a survey. In NATO Science Series, 2006.Google Scholar
  9. 9.
    T.F. Cootes, G.J. Edwards, and C.J. Taylor. Active appearance models. IEEE Transaction on Pattern Analysis an Machine Intelligence, 23:681–685, 2001.CrossRefGoogle Scholar
  10. 10.
    D. Cristinacce and T. Cootes. Feature Detection and Tracking with Constrained Local Models. Proceedings of the British Machine Vision Conf, 2006.Google Scholar
  11. 11.
    DaimerChryslerAG. The electronic drawbar, June 2001. URL
  12. 12.
    DaimlerChrysler. Driver assistant with an eye for the essentials. URL
  13. 13.
    B. Delaunay. Sur la sphere vide. Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk, 7: 793–800, 1934.Google Scholar
  14. 14.
    D. Dinges and F. Perclos: A valid psychophysiological measure of alertness as assesed by psychomotor vigilance. Technical Report MCRT-98-006, Federal Highway Administration. Office of motor carriers, 1998.Google Scholar
  15. 15.
    European Project FP6 (IST-1-507674-IP). AIDE – Adaptive Integrated Driver-Vehicle Interface, 2004–2008. URL
  16. 16.
    European Project FP6 (IST-2002- Advanced sensor development for attention, stress, vigilance and sleep/wakefulness monitoring (SENSATION), 2004–2007. URL
  17. 17.
    A.W. Fitzgibbon and R.B. Fisher. A buyer’s guide to conic fitting. In Proceedings of the 6th British Conference on Machine Vision, volume 2, pp. 513–522, Birmingham, United Kingdom, 1995.Google Scholar
  18. 18.
    D.A. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall, 2003.Google Scholar
  19. 19.
    R. Grace. Drowsy driver monitor and warning system. In International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Aug 2001.Google Scholar
  20. 20.
    S. Guillaume and B. Charnomordic. A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference systems from data. Studies in Fuzziness and Soft Computing, 128:148–175, 2003.Google Scholar
  21. 21.
    H. Ueno, M. Kaneda, and M. Tsukino. Development of drowsiness detection system. In Proceedings of Vehicle Navigation and Information Systems Conference, pp. 15–20, 1994.Google Scholar
  22. 22.
    AWAKE Consortium (IST 2000-28062). System for Effective Assessment of Driver Vigilance and Warning According to Traffic Risk Estimation – AWAKE, Sep 2001–2004. URL
  23. 23.
    Q. Ji and X. Yang. Real-time eye, gaze and face pose tracking for monitoring driver vigilance. Real-Time Imaging, 8:357–377, Oct 2002.zbMATHCrossRefGoogle Scholar
  24. 24.
    A. Kircher, M. Uddman, and J. Sandin. Vehicle control and drowsiness. Technical Report VTI-922A, Swedish National Road and Transport Research Institute, 2002.Google Scholar
  25. 25.
    D. Koons and M. Flicker. IBM Blue Eyes project, 2003. URL
  26. 26.
    M. Kutila. Methods for Machine Vision Based Driver Monitoring Applications. Ph.D. thesis, VTT Technical Research Centre of Finland, 2006.Google Scholar
  27. 27.
    Y. Matsumoto and A. Zelinsky. An algorithm for real-time stereo vision implementation of head pose and gaze direction measurements. In Proceedings of IEEE 4th International Conference Face and Gesture Recognition, pp. 499–505, Mar 2000.Google Scholar
  28. 28.
    I. Matthews and S. Baker. Active appearance models revisited. International Journal of Computer Vision, 60(2):135–164, November 2004.CrossRefGoogle Scholar
  29. 29.
    J.A. Nelder and R. Mead. A simplex method for function minimization. Computer Journal, 7(4):308–313, 1965.zbMATHGoogle Scholar
  30. 30.
    J. Nuevo, L.M. Bergasa, M.A. Sotelo, and M. Ocana. Real-time robust face tracking for driver monitoring. Intelligent Transportation Systems Conference, 2006. ITSC’06. IEEE, pp. 1346–1351, 2006.Google Scholar
  31. 31.
    L. Nunes and M.A. Recarte. Cognitive demands of hands-free phone conversation while driving, Chap. F5, pp. 133–144. Pergamon, Oxford, 2002.Google Scholar
  32. 32.
    P. Rau. Drowsy driver detection and warning system for commercial vehicle drivers: Field operational test design, analysis and progress, NHTSA, 2005.Google Scholar
  33. 33.
    D. Royal. Volume I – Findings; National Survey on Distracted and Driving Attitudes and Behaviours, 2002. Technical Report DOT HS 809 566, The Gallup Organization, March 2003.Google Scholar
  34. 34.
    Seeing Machines. Facelab transport, August 2006. URL
  35. 35.
    Seeing Machines. Driver state sensor, August 2007. URL
  36. 36.
    W. Shih and Liu. A calibration-free gaze tracking technique. In Proceedings of 15th Conference Patterns Recognition, volume 4, pp. 201–204, Barcelona, Spain, 2000.Google Scholar
  37. 37.
    P. Smith, M. Shah, and N.Da.V. Lobo. Determining driver visual attention with one camera. IEEE Transaction on Intelligent Transportation Systems, 4(4):205–218, 2003.CrossRefGoogle Scholar
  38. 38.
    T. Victor, O. Blomberg, and A. Zelinsky. Automating the measurement of driver visual behaviours using passive stereo vision. In Proceedings of Intelligent Conference Series Vision in Vehicles VIV9, Brisbane, Australia, Aug 2001.Google Scholar
  39. 39.
    Volvo Car Corporation. Driver alert control. URL
  40. 40.
    W. Wierwille, L. Tijerina, S. Kiger, T. Rockwell, E. Lauber, and A. Bittne. Final report supplement – task 4: Review of workload and related research. Technical Report DOT HS 808 467(4), USDOT, Oct 1996.Google Scholar
  41. 41.
    W. Wierwille, Wreggit, Kirn, Ellsworth, and Fairbanks. Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness, final report; technical reports & papers. Technical Report DOT HS 808 247, USDOT, Dec 1994. URL

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Luis M. Bergasa
    • 1
  • Jesús Nuevo
    • 1
  • Miguel A. Sotelo
    • 1
  • Rafael Barea
    • 1
  • Elena Lopez
    • 1
  1. 1.Department of ElectronicsUniversity of AlcalaSpain

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