Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Real-time eye tracking for the assessment of driver fatigue

Loading full text...

Full text loading...

/deliver/fulltext/htl/5/2/HTL.2017.0020.html;jsessionid=15f1jb90laovj.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fhtl.2017.0020&mimeType=html&fmt=ahah

References

    1. 1)
    2. 2)
      • 9. Lang, L., Qi, H.: ‘The study of driver fatigue monitor algorithm combined PERCLOS and AECS’. Int. Conf. on Computer Science and Software Engineering, 2008, pp. 349352.
    3. 3)
      • 5. Li, D.H., Liu, Q., Yuan, W., et al: ‘Relationship between fatigue driving and traffic accident’, J. Traffic Transp. Eng., 2010, 2, pp. 104109.
    4. 4)
      • 32. Fan, X., Sun, Y., Yin, B.: ‘Driver fatigue detection based on AdaBoost global features’, J. Comput. Inf. Syst., 2009, 5, (1), pp. 6168.
    5. 5)
      • 29. Punitha, A., Geetha, M.K., Sivaprakash, A.: ‘Driver fatigue monitoring system based on eye state analysis’. Int. Conf. Circuit, Nagercoil, India, March 2014, pp. 14051408.
    6. 6)
    7. 7)
    8. 8)
      • 4. Saini, V., Saini, R.: ‘Driver drowsiness detection system and techniques: a review’, Int. J. Comput. Sci. Inf. Technol., 2014, 5, (3), pp. 42454249.
    9. 9)
      • 36. Hu, J.F.: ‘Comparison of different features and classifiers for driver fatigue detection based on a single EEG channel’, Comput. Math. Methods Med., 2017, 2017, (3), pp. 19, doi: 10.1155/2017/5109530.
    10. 10)
      • 7. Schmidt, E., Decke, R., Rasshofer, R.: ‘Correlation between subjective driver state measures and psychophysiological and vehicular data in simulated driving’. Intelligent Vehicles Symp., Gothenburg, Sweden, June 2016, pp. 13801385.
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • 11. Trutschel, U., Sirois, B., Sommer, D., et al: ‘PERCLOS: an alertness measure of the past’. Driving Assessment 2011: 6th Int. Driving Symp. Human Factors in Driver Assessment, Training, and Vehicle Design, 2011.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • 6. Wang, Q., Yang, J., Ren, M.: ‘Driver fatigue detection: a survey’. The Sixth World Congress on IEEE Intelligent Control and Automation, Dalian, China, June 2006, vol. 2, pp. 85878591.
    25. 25)
      • 25. Choi, I.H., Hong, S.K., Kim, Y.G.: ‘Real-time categorization of driver's gaze zone using the deep learning techniques’. Int. Conf. Big Data and Smart Computing, 2016, pp. 143148.
    26. 26)
    27. 27)
      • 21. Vapnik, V.: ‘Estimation of dependences based on empirical data’ (Springer, New York, NY, 2006).
    28. 28)
      • 10. Dong, Y., Hu, Z., Uchimura, K., et al: ‘Driver inattention monitoring system for intelligent vehicles: a review’. Intelligent Vehicles Symp., 2009, pp. 875880.
    29. 29)
      • 31. Hemadri, V.B., Kulkarni, U.P.: ‘Detection of drowsiness using fusion of yawning and eyelid movements’, Commun. Comput. Inf. Sci., 2013, 361, pp. 583594.
    30. 30)
    31. 31)
    32. 32)
      • 23. Jing-Jing, L.V.: ‘Fatigue recognition based on adaptive locality preserving projections’, Comput. Eng. Appl., 2010, 46, (22), pp. 187189.
    33. 33)
      • 24. Lin, L., Huang, C., Ni, X., et al: ‘Driver fatigue detection based on eye state’, Technol. Health Care Off. J. Eur. Soc. Eng. Med., 2015, 23, (s2), pp. S453S463.
    34. 34)
    35. 35)
    36. 36)
    37. 37)
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2017.0020
Loading

Related content

content/journals/10.1049/htl.2017.0020
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address