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Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye

  • Erick P. Herrera-Granda
  • Jorge A. Caraguay-Procel
  • Pedro D. Granda-Gudiño
  • Israel D. Herrera-Granda
  • Leandro L. Lorente-LeyvaEmail author
  • Diego H. Peluffo-Ordóñez
  • Javier Revelo-Fuelagán
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)

Abstract

At a global level, drowsiness is one of the main causes of road accidents causing frequent deaths and economic losses. To solve this problem an application developed in Matlab environment was made, which processes real time acquired images in order to determine if the driver is awake or drowsy. Using AdaBoost training Algorithm for Viola-Jones eyes detection, a cascade classifier finds the location and the area of the driver eyes in each frame of the video. Once the driver eyes are detected, they are analyzed whether are open or closed by color segmentation and thresholding based on the sclera binarized area. Finally, it was implemented as a drowsiness detection system which aims to prevent driver fall asleep while driving a vehicle by activating an audible alert, reaching speeds up to 14.5 fps.

Keywords

Drowsiness detection Image processing Artificial intelligence Human eye Alarm 

Notes

Acknowledgment

The authors acknowledge to the research project “Desarrollo de una metodología de visualización interactiva y eficaz de información en Big Data” supported by Agreement No. 180 November 1st, 2016 by VIPRI from Universidad de Nariño. Also, authors thank the valuable support given by the SDAS Research Group (www.sdas-group.com) and Facultad de Ingeniería en Ciencias Aplicadas from Universidad Técnica del Norte, Ibarra, Ecuador.

References

  1. 1.
    Horne, J., Reyner, L.: Driver sleepiness. J. Sleep Res. 4, 23–29 (1995)CrossRefGoogle Scholar
  2. 2.
    Garcés, M., Salgado, J., Cruz, J., Cafión, W.: Sistemas de detección de somnolencia en conductores: inicio, desarrollo y futuro. Ingeniería y Región 13, 159–168 (2015)CrossRefGoogle Scholar
  3. 3.
    Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors (Switzerland) 12(12), 16937–16953 (2012)CrossRefGoogle Scholar
  4. 4.
    Vural, E., Cetin, M., Ercil, A., Littlewort, G., Bartlett, M., Movellan, J.: Drowsy driver detection through facial movement analysis. In: Lew, M., Sebe, N., Huang, T.S., Bakker, E.M. (eds.) HCI 2007. LNCS, vol. 4796, pp. 6–18. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75773-3_2CrossRefGoogle Scholar
  5. 5.
    El-Den, B.M., Mohamed, M.A., AbdelFattah, A.I.: Safe vehicle driving using android based smartphones. In: Abraham, A., Jiang, X.H., Snášel, V., Pan, J.-S. (eds.) Intelligent Data Analysis and Applications. AISC, vol. 370, pp. 291–303. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21206-7_25CrossRefGoogle Scholar
  6. 6.
    Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., Babiloni, F.: Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci. Biobehav. Rev. 44, 58–75 (2014)CrossRefGoogle Scholar
  7. 7.
    Liu, C., Hosking, G., Lenné, M.: Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J. Safety Res. 40(4), 239–245 (2009)CrossRefGoogle Scholar
  8. 8.
    Hyun, J., Mao, Z.H., Tijerina, L., Pilutti, T., Coughlin, J., Feron, E.: Detection of driver fatigue caused by sleep deprivation. IEEE Trans. Syst. Man Cybernetics Part A: Syst. Humans 39(4), 694–705 (2009)CrossRefGoogle Scholar
  9. 9.
    Powell, B., Chau, K.: Sleepy driving. Med. Clin. North Am. 94(3), 531–540 (2010)CrossRefGoogle Scholar
  10. 10.
    Correa, A.G., Orosco, L., Laciar, E.: Automatic detection of drowsiness in EEG records based on multimodal analysis. Med. Eng. Phys. 36(2), 244–249 (2014)CrossRefGoogle Scholar
  11. 11.
    Dajeong, K., Hyungseob, H., Sangjin, C., Uipil, C.: Detection of drowsiness with eyes open using EEG-based power spectrum analysis. In: 7th International Forum on Strategic Technology (IFOST), pp. 1–4 (2012)Google Scholar
  12. 12.
    Reddy, B., Kim, Y.H., Yun, S., Seo, C., Jang, J.: Drowsiness detection for embedded system using model compresssion of deep neuronal networks. In: Computer Vision Foundation CVF, pp. 121–128 (2017)Google Scholar
  13. 13.
    Montanini, L., Gambi, E., Spinsante, S.: An OpenCV based android application for drowsiness detection on mobile devices. In: Conti, M., Martínez Madrid, N., Seepold, R., Orcioni, S. (eds.) Mobile Networks for Biometric Data Analysis. LNEE, vol. 392, pp. 145–158. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39700-9_12CrossRefGoogle Scholar
  14. 14.
    Lee, B., Jung, S., Chung, W.: Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection. IET Commun. 5(17), 2461–2469 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE CVPR (2001)Google Scholar
  16. 16.
    Muzamil, S., Singh, D., Vandhan, V.: Impact of gradient ascent and boosting algorithm in classification. Int. J. Intell. Eng. Syst. 11(1), 41–49 (2018)Google Scholar
  17. 17.
    Wang, M., Guo, L., Chen, W.Y.: Blink detection using Adaboost and contour circle for fatigue recognition. Comput. Electr. Eng. 58(1), 502–512 (2017)CrossRefGoogle Scholar
  18. 18.
    Bo, S., Chen, S., Wang, J., Chen, H.: A robust multi-class AdaBoost algorithm for mislabeled noisy data. Knowl.-Based Syst. 102(1), 87–102 (2016)Google Scholar
  19. 19.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: IEEE ICIP, pp. 900–903 (2002)Google Scholar
  20. 20.
    Wonji, L., Jun, C.H., Lee, J.S.: Instance categorization by support vector machines to adjust weights in AdaBoost for imbalanced data classification. Inf. Sci. 381(1), 92–103 (2017)Google Scholar
  21. 21.
    Egorov, A.: Algorithm for optimization of Viola–Jones object detection framework parameters. J. Phy.: Conf. Ser. 945(1) (2018)Google Scholar
  22. 22.
    Lescano, G., Santana, P., Costaguta, R.: Analysis of a GPU implementation of Viola-Jones’ algorithm for features selection. J. Comput. Sci. Technol. 17(1), (2017)Google Scholar
  23. 23.
    Wu, B., Ai, H., Huang, C., Lao, S.: Fast rotation invariant multi-view face detection based on real adaboost. In: Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 79–84 (2004)Google Scholar
  24. 24.
    Freund, Y., Schapire, R.: A short introduction to boosting. J. Japanese Soci. Artif. Intell. 14(5), 771–780 (1999)Google Scholar
  25. 25.
    Tsai, P., Hsu, Y., Chiu, C., Chu, T.: Accelerating AdaBoost algorithm using GPU for multi-object recognition. In: 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 738–741 (2015)Google Scholar
  26. 26.
    Lorente-Leyva, L.L., et al.: Developments on solutions of the normalized-cut-clustering problem without eigenvectors. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds.) ISNN 2018. LNCS, vol. 10878, pp. 318–328. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92537-0_37CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Erick P. Herrera-Granda
    • 1
  • Jorge A. Caraguay-Procel
    • 1
  • Pedro D. Granda-Gudiño
    • 1
  • Israel D. Herrera-Granda
    • 1
  • Leandro L. Lorente-Leyva
    • 1
    Email author
  • Diego H. Peluffo-Ordóñez
    • 2
  • Javier Revelo-Fuelagán
    • 3
  1. 1.Facultad de Ingeniería en Ciencias AplicadasUniversidad Técnica del NorteIbarraEcuador
  2. 2.Escuela de Ciencias Matemáticas y Tecnología InformáticaYachay TechSan Miguel de UrcuquíEcuador
  3. 3.Universidad de NariñoPastoColombia

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