Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 154–164Cite as

Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

Abstract

Statistics have shown that \(20\%\) of all road accidents are fatigue-related, and drowsy detection is a car safety algorithm that can alert a snoozing driver in hopes of preventing an accident. This paper proposes a deep architecture referred to as deep drowsiness detection (DDD) network for learning effective features and detecting drowsiness given a RGB input video of a driver. The DDD network consists of three deep networks for attaining global robustness to background and environmental variations and learning local facial movements and head gestures important for reliable detection. The outputs of the three networks are integrated and fed to a softmax classifier for drowsiness detection. Experimental results show that DDD achieves \(73.06\%\) detection accuracy on NTHU-drowsy driver detection benchmark dataset.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://cv.cs.nthu.edu.tw/php/callforpaper/2016_ACCVworkshop/.

References

  1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)

    Google Scholar 

  2. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)

    Google Scholar 

  3. Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: CVPR, pp. 2625–2634 (2015)

    Google Scholar 

  4. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24673-2_3

    Chapter  Google Scholar 

  5. Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)

  6. Li, W., He, Q.C., Fan, X.M., Fei, Z.M.: Evaluation of driver fatigue on two channels of EEG data. Neurosci. Lett. 506, 235–239 (2012)

    Article  Google Scholar 

  7. Patel, M., Lal, S.K.L., Kavanagh, D., Rossiter, P.: Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst. Appl. 38, 7235–7242 (2011)

    Article  Google Scholar 

  8. Mattsson, K.: In vehicle prediction of truck driver sleepiness. Master’s thesis, Luleå University of Technology, vol. 107 (2007)

    Google Scholar 

  9. Boyle, L.N., Tippin, J., Paul, A., Rizzo, M.: Driver performance in the moments surrounding a microsleep. Transp. Res. Part F: Traffic Psychol. Behav. 11, 126–136 (2008)

    Article  Google Scholar 

  10. Friedrichs, F., Yang, B.: Drowsiness monitoring by steering and lane data based features under real driving conditions. In: 2010 18th European Signal Processing Conference, pp. 209–213 (2010)

    Google Scholar 

  11. Forsman, P.M., Vila, B.J., Short, R.A., Mott, C.G., Dongen, H.P.: Efficient driver drowsiness detection at moderate levels of drowsiness. Accid. Anal. Prev. 50, 341–350 (2013)

    Article  Google Scholar 

  12. Dwivedi, K., Biswaranjan, K., Sethi, A.: Drowsy driver detection using representation learning. In: 2014 IEEE International Advance Computing Conference (IACC), pp. 995–999 (2014)

    Google Scholar 

  13. Lee, S.J., Jo, J., Jung, H.G., Park, K.R., Kim, J.: Real-time gaze estimator based on driver’s head orientation for forward collision warning system. IEEE Trans. Intell. Transp. Syst. 12, 254–267 (2011)

    Article  Google Scholar 

  14. Choi, I.H., Hong, S.K., Kim, Y.G.: Real-time categorization of driver’s gaze zone using the deep learning techniques. In: International Conference on Big Data and Smart Computing (BigComp), pp. 143–148 (2016)

    Google Scholar 

  15. Singh, M., Kaur, G.: Drowsy detection on eye blink duration using algorithm. Int. J. Emerg. Tech. Adv. Eng. 2, 363–365 (2012)

    Google Scholar 

  16. Saito, H., Ishiwaka, T., Okabayashi, S.: Applications of driver’s line of sight to automobiles-what can driver’s eye tell. In: 1994 Proceedings of Vehicle Navigation and Information Systems Conference, pp. 21–26 (1994)

    Google Scholar 

  17. Horng, W.B., Chen, C.Y., Chang, Y., Fan, C.H.: Driver fatigue detection based on eye tracking and dynamk, template matching. In: 2004 IEEE International Conference on Networking, Sensing and Control, vol. 1, pp. 7–12 (2004)

    Google Scholar 

  18. Smith, P., Shah, M., da Vitoria Lobo, N.: Monitoring head/eye motion for driver alertness with one camera. In: ICPR, p. 4636 (2000)

    Google Scholar 

  19. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6, 21–45 (2006)

    Article  Google Scholar 

  20. Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2010)

    Article  Google Scholar 

  21. Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)

    MATH  Google Scholar 

  22. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  23. Vedaldi, A., Lenc, K.: Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 689–692 (2015)

    Google Scholar 

  24. Sahayadhas, A., Sundaraj, K., Murugappan, M.: Electromyogram signal based hypovigilance detection. Biomed. Res. 25, 281–288 (2014)

    Google Scholar 

  25. Chieh, T.C., Mustafa, M.M., Hussain, A., Hendi, S.F., Majlis, B.Y.: Development of vehicle driver drowsiness detection system using electrooculogram (EOG). In: 1st International Conference on Computers, Communications, Signal Processing with Special Track on Biomedical Engineering, pp. 165–168 (2005)

    Google Scholar 

Download references

Acknowledgement

This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028680).

Author information

Authors and Affiliations

  1. School of Electrical Engineering, KAIST, Guseong-dong, Yuseong-gu, Dajeon, Republic of Korea

    Sanghyuk Park, Fei Pan, Sunghun Kang & Chang D. Yoo

Authors
  1. Sanghyuk Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sunghun Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chang D. Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanghyuk Park .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Park, S., Pan, F., Kang, S., Yoo, C.D. (2017). Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54526-4_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation