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An on-board vision based system for drowsiness detection in automotive drivers

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Abstract

This paper proposes a system for on-board monitoring the loss of attention of an automotive driver, based on PERcentage of eye CLOSure (PERCLOS). This system has been developed considering the practical on-board constraints such as illumination variation, poor illumination conditions, free movement of driver’s face, limitations in algorithms etc. A novel framework for PERCLOS computation is reported in this paper. The system consists of an embedded processing unit, a camera, a near infra-red lighting system, power supply, a set of speakers and a voltage regulation unit. The image based algorithm is based on the PERCLOS as an indicator of the loss of attention of the driver. The authenticity of PERCLOS as an indicator of drowsiness has been validated using EEG signals.

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References

  1. Arun, S., Sundaraj, K., Murugappan, M.: Driver inattention detection methods: a review. In 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (2012)

  2. Matousek, M., Petersén, A.: A method for assessing alertness fluctuations from EEG spectra. Electroencephalogr Clin. Neurophysiol. 55(1), 108–113 (1983)

    Google Scholar 

  3. Kar, S., Bhagat, M., Routray, A.: EEG signal analysis for the assessment and quantification of driver’s fatigue. Transp. Res. 13, 297–306 (2010)

    Google Scholar 

  4. Routray, A., Kar, S., Bhagat, M.: EEG analysis for the assessment and quantification of driver’s fatigue. Transp. Res. Part F Traffic Psychol. Behav. 13(5), 297–306 (2010)

    Google Scholar 

  5. Cajochen, C., Zeitzer, J.M., Czeisler, C., Dijk, D.J.: Dose–response relationship for light intensity and ocular and electroencephalographic correlates of human alertness. Behav. Brain Res. 115, 75–83 (2000)

    Article  Google Scholar 

  6. Knipling, R. R., Wierwille, W. W.: Vehicle-based drowsy driver detection: current status and future prospects. IVHS America fourth annual meeting, pp. 2–24, April 1994

  7. Dhupati, L., Kar, S., Rajaguru, A., Routray, A.: A novel drowsiness detection scheme based on speech analysis with validation using simultaneous EEG recordings. 2010 IEEE Conference on Automation Science and Engineering (CASE) (2010)

  8. Bundele, M. M., Banerjee, R.: Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: a neural network approach. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (2009)

  9. Onken, R.: DAISY, an adaptive, knowledge-based driver monitoring and warning system, In Proceedings of the Intelligent Vehicles ‘94 Symposium (1994)

  10. Singh, S., Papanikolopoulos, N.: Monitoring driver fatigue using facial analysis techniques. In Proceedings of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (1999)

  11. Ayoob, E. M., Steinfeld, A., Grace, R.: Identification of an “appropriate” drowsy driver detection interface for commercial vehicle operations. In Proceedings of The Human Factors and Ergonomics Society Annual Meeting (2003)

  12. Fletcher, L., Petersson, L., Zelinsky, A.: Driver assistance systems based on vision in and out of vehicles. In Proceedings IEEE on Intelligent Vehicles Symposium (2003)

  13. Smith, P., Lobo, N.V., Shah, M.: Determining driver visual attention with one camera. IEEE Trans. Intell. Transp. Syst. 4(4), 205–218 (2003)

    Article  Google Scholar 

  14. Zhu, Z., Ji, Q.: Real time and non-intrusive driver fatigue monitoring. In IEEE Intelligent Transportation Systems Conference, Washington, DC (2004)

  15. Bergasa, L.M., Nuevo, J., Sotelo, M.A., Barea, R., Lopez, M.E.: Real-time system for monitoring driver vigilance. IEEE Trans. Intell. Transp. Syst. 7(1), 63–77 (2006)

    Article  Google Scholar 

  16. Eriksson, M., Papanikolopoulos, N.P.: Driver fatigue: a vision-based approach to automatic diagnosis. Transp. Res. Part C 9(6), 399–413 (2001)

    Article  Google Scholar 

  17. Sacco, M., Farrugia, R.: Driver fatigue monitoring system using support vector machines. In 5th International Symposium on Communications Control and Signal Processing (ISCCSP) (2012)

  18. Dinges, D., Mallis, M. M., Maislin, G., Powell, J. W., and National Highway Traffic Safety Administration: Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management. (1998)

  19. Dingus, T.A., Hardee, H., Wierwile, W.W.: Development of models for on-board detection of driver impairment. Accid. Anal. Prev. 19(4), 271–283 (1987)

    Article  Google Scholar 

  20. Viola, P., Jones, M.M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  21. Gupta, S., Dasgupta, A., Routray, A.: Analysis of training of parameters in Haar. In IEEE International Conference on Image Information Processing, Shimla (2011)

  22. Mohanty, P.K., Sarkar, S., Kasturi, R.: Designing affine transformations based face recognition algorithms. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)

  23. Lien, J.J., Kanade, T., Cohn, J.F., Li, C.: Detection, tracking, and classification of action units in facial expression. Robotics Auton. Syst. 31(3), 131–146 (2000)

    Article  Google Scholar 

  24. Asteriadis, S., Nikolaidis, N., Hajdu, A., Pitas, I.: An eye detection algorithm using pixel to edge information. In ICSSP (2006)

  25. Huang, J., Wechsler, H.: Eye detection using optimal wavelet packets and radial basis functions (RBFs). Int. J. Pattern Recognit. Artif. Intell. 13(7), 1–18 (1999)

    Google Scholar 

  26. Song, J., Chia, Z., Liub, J.: A robust eye detection method using combined binary edge and intensity information. Patt. Recognit. 39, 1110–1125 (2006)

    Article  MATH  Google Scholar 

  27. Zhou, Z., Geng, X.: Projection functions for eye detection. Pattern Recognit. 37, 1049–1056 (2003)

    Article  Google Scholar 

  28. Wang, P., Green, M. B, Ji, Q., Wayman, J.: Automatic eye detection and its validation. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)

  29. Hadid, A., Heikkila, J., Silven, O., Pietikainen, M.: Face and eye detection for person authentication in mobile phones. In First ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC ‘07) (2007)

  30. Liu, X., Xu, F., Fujimura, K.: Real-time eye detection and tracking for driver observation under various light conditions. In IEEE Intelligent Vehicle Symposium (2002)

  31. Bhowmick, B., Kumar, K. S. C.: Detection and classification of eye state in IR camera for driver drowsiness identification. In IEEE International Conference on Signal and Image Processing Applications (2009)

  32. Eriksson, M., Papanikolopoulos, N. P.: Eye-tracking for detection of driver fatigue. In Proceedings of the International Conference on Intelligent Transportation Systems, Boston (1998)

  33. Sirohey, S.A., Rosenfeld, A.: Eye detection in a face image using linear and nonlinear filters. Pattern Recognit. 34(7), 1367–1391 (2001)

    Article  MATH  Google Scholar 

  34. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  35. Jap, B.T., Lal, S., Fischer, P., Bekiaris, E.: Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst. Appl. 36(2), 2352–2359 (2009)

    Article  Google Scholar 

  36. Papadelis, C., Kourtidou-Papadelis, C., Panagiotis, D., Bamidis, D., Chouvarda, I., Koufogiannis, D.: Indicators of sleepiness in an ambulatory EEG study of night driving. In 28th IEEE EMBS Annual International Conference, New York, Aug 30–Sep 3, (2006)

  37. Papadelis, C., Chen, Z., Kourtidou-Papadelis, C., Bamidis, D., Chouvarda, I., Koufogiannis, D.: Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep deprived traffic accidents. Clin. Neurophysiol. 118(9), 1906–1922 (2007)

    Article  Google Scholar 

  38. Kar, S., Routray, A.: Analysis of electroencephalograph signals for detecting fatigue in human drivers, PhD Dissertation (2012)

  39. Nunej, P., Srinivasan, R.: Electric fields of the brain: the neurophysics of EEG, 2nd edn. Oxford University Press, New York (2006)

    Google Scholar 

  40. Singvi, M., Dasgupta, A., Routray, A.: A real time algorithm for detection of spectacles leading to eye detection. In 4th International Conference on Intelligent Human Computer Interaction (IHCI) (2012)

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Acknowledgments

The funds received from the Department of Electronics and Information Technology, Government of India, for this study is gratefully acknowledged. The authors would like to thank the subjects for voluntary participation in the experiment for creation of the database.

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Correspondence to Anirban Dasgupta.

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Dasgupta, A., George, A., Happy, S.L. et al. An on-board vision based system for drowsiness detection in automotive drivers. Int J Adv Eng Sci Appl Math 5, 94–103 (2013). https://doi.org/10.1007/s12572-013-0086-2

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  • DOI: https://doi.org/10.1007/s12572-013-0086-2

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