Skip to main content

Driver’s Drowsiness Detection Through Computer Vision: A Review

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10633)

Abstract

Drowsiness and sleepiness of driver is an important cause of road accident on expressways, highways, and motorways. These accidents not only results in economic loss but may also in physical injuries, which could result permanent disability or even death. The aim of this research is to minimize this cause of road accidents. Safe driving requirement is unavoidable and to attain this, driver’s drowsiness detection system is to be incorporate in vehicles. Drowsiness detection using vehicle-based, physiological, and behavioral change measurement system is possible with embedded pros and cons. Advancements in the field of image processing and development of faster and cheaper processors direct researches to focus on behavioral change measurement system for drowsiness detection. Computer vision based drowsiness detection is possible by closely monitoring the drowsiness symptoms like eye blinking intervals, yawning, eye closing duration, head position etc. The presented paper deals with merits and demerits of the drowsiness symptoms measurement mechanism and computer vision based drowsiness detection systems. The conclusion of the research is that by designing a hybrid computer vision based drowsy driver detection system dependability achieved. The proposed system is non-intrusive in nature and helpful in attaining safer roads by limiting potential accidental threat due to driver drowsiness.

Keywords

  • Drowsiness detection
  • Computer vision
  • Image processing
  • Eye blinking
  • Yawning
  • PERCLOS

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-02840-4_22
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-02840-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

References

  1. World Health Organization: Global status report on road safety 2015. World Health Organization (2015)

    Google Scholar 

  2. Peden, M., Toroyan, T., Krug, E., Iaych, K.: The status of global road safety: the agenda for sustainable development encourages urgent action. J. Australas. Coll. Road Saf. 27, 37 (2016)

    Google Scholar 

  3. Arvind, P.D., Jivaji, M.J., Romi, K., Kamble, P.: Accident informer and prevention system. Int. J. Eng. Sci. 7, 4772 (2017)

    Google Scholar 

  4. Murata, A., Urakami, Y., Moriwaka, M.: An attempt to prevent traffic accidents due to drowsy driving-prediction of drowsiness by Bayesian estimation. In: 2014 Proceedings of the SICE Annual Conference (SICE), pp. 1708–1715. IEEE (2014)

    Google Scholar 

  5. Ahmed, R., Emon, K.E.K., Hossain, M.F.: Robust driver fatigue recognition using image processing. In: 2014 International Conference on Informatics, Electronics & Vision (ICIEV), pp. 1–6. IEEE (2014)

    Google Scholar 

  6. Gonçalves, M., et al.: Sleepiness at the wheel across Europe: a survey of 19 countries. J. Sleep Res. 24, 242–253 (2015)

    CrossRef  Google Scholar 

  7. Toda, T., Suzuki, K., Chen, G., Takami, I.: Robust control of active suspension—Improvement of ride comfort and driving stability using half car model. In: 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 548–553. IEEE (2015)

    Google Scholar 

  8. 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)

    CrossRef  Google Scholar 

  9. Correa, A.G., Orosco, L., Laciar, E.: Automatic detection of drowsiness in EEG records based on multimodal analysis. Med. Eng. Phys. 36, 244–249 (2014)

    CrossRef  Google Scholar 

  10. Jo, J., Lee, S.J., Park, K.R., Kim, I.-J., Kim, J.: Detecting driver drowsiness using feature-level fusion and user-specific classification. Expert Syst. Appl. 41, 1139–1152 (2014)

    CrossRef  Google Scholar 

  11. Saini, V., Saini, R.: Driver drowsiness detection system and techniques: a review. Int. J. Comput. Sci. Inf. Technol. 5, 4245–4249 (2014)

    Google Scholar 

  12. Viljoen, E., Visser, J., Koen, N., Musekiwa, A.: A systematic review and meta-analysis of the effect and safety of ginger in the treatment of pregnancy-associated nausea and vomiting. Nutr. J. 13, 20 (2014)

    CrossRef  Google Scholar 

  13. Veenendaal, A., Daly, E., Jones, E., Gang, Z., Vartak, S., Patwardhan, R.S.: Multi-view point drowsiness and fatigue detection. Comput. Sci. Emerg. Res. J. 2 (2014)

    Google Scholar 

  14. Murata, A., Naitoh, K., Karwowski, W.: A method for predicting the risk of virtual crashes in a simulated driving task using behavioural and subjective drowsiness measures. Ergonomics 60, 714–730 (2017)

    CrossRef  Google Scholar 

  15. Dissanayaka, C., et al.: Comparison between human awake, meditation and drowsiness EEG activities based on directed transfer function and MVDR coherence methods. Med. Biol. Eng. Comput. 53, 599–607 (2015)

    CrossRef  Google Scholar 

  16. Nguyen, T., Ahn, S., Jang, H., Jun, S.C., Kim, J.G.: Utilization of a combined EEG/NIRS system to predict driver drowsiness. Sci. Rep. 7, 43933 (2017)

    CrossRef  Google Scholar 

  17. Wu, D., Lawhern, V.J., Gordon, S., Lance, B.J., Lin, C.-T.: Driver drowsiness estimation from EEG signals using online weighted adaptation regularization for regression (OwARR). IEEE Trans. Fuzzy Syst. 25, 1522–1535 (2016)

    CrossRef  Google Scholar 

  18. Wang, X., Xu, C.: Driver drowsiness detection based on non-intrusive metrics considering individual specifics. Accid. Anal. Prev. 95, 350–357 (2016)

    CrossRef  Google Scholar 

  19. Lawoyin, S., Fei, D.-Y., Bai, O.: Accelerometer-based steering-wheel movement monitoring for drowsy-driving detection. Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 229, 163–173 (2015)

    CrossRef  Google Scholar 

  20. Åkerstedt, T., Hallvig, D., Kecklund, G.: Normative data on the diurnal pattern of the Karolinska sleepiness scale ratings and its relation to age, sex, work, stress, sleep quality and sickness absence/illness in a large sample of daytime workers. J. Sleep Res. 26, 559–566 (2017)

    CrossRef  Google Scholar 

  21. Rumagit, A.M., Akbar, I.A., Igasaki, T.: Gazing time analysis for drowsiness assessment using eye gaze tracker. Telkomnika: J. Telecomun. Comput. Electron. Control 15(2), 919–925 (2017)

    CrossRef  Google Scholar 

  22. Forsman, P., Pyykkö, I., Toppila, E., Hæggström, E.: Feasibility of force platform based roadside drowsiness screening–a pilot study. Accid. Anal. Prev. 62, 186–190 (2014)

    CrossRef  Google Scholar 

  23. Jackson, M.L., et al.: The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness. Accid. Anal. Prev. 87, 127–133 (2016)

    CrossRef  Google Scholar 

  24. Zhu, X., Zheng, W.-L., Lu, B.-L., Chen, X., Chen, S., Wang, C.: EOG-based drowsiness detection using convolutional neural networks. In: IJCNN, pp. 128–134 (2014)

    Google Scholar 

  25. Cona, F., Pizza, F., Provini, F., Magosso, E.: An improved algorithm for the automatic detection and characterization of slow eye movements. Med. Eng. Phys. 36, 954–961 (2014)

    CrossRef  Google Scholar 

  26. Chui, K.T., Tsang, K.F., Chi, H.R., Ling, B.W.K., Wu, C.K.: An accurate ECG-based transportation safety drowsiness detection scheme. IEEE Trans. Industr. Inf. 12, 1438–1452 (2016)

    CrossRef  Google Scholar 

  27. Jung, S.-J., Shin, H.-S., Chung, W.-Y.: Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell. Transport Syst. 8, 43–50 (2014)

    CrossRef  Google Scholar 

  28. Khushaba, R.N., Kodagoda, S., Lal, S., Dissanayake, G.: Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans. Biomed. Eng. 58, 121–131 (2011)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  30. Lin, F.-C., Ko, L.-W., Chuang, C.-H., Su, T.-P., Lin, C.-T.: Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system. IEEE Trans. Circ. Syst. I Regul. Pap. 59, 2044–2055 (2012)

    MathSciNet  CrossRef  Google Scholar 

  31. Hu, S., Zheng, G.: Driver drowsiness detection with eyelid related parameters by support vector machine. Expert Syst. Appl. 36, 7651–7658 (2009)

    CrossRef  Google Scholar 

  32. Chen, L.-L., Zhao, Y., Zhang, J., Zou, J.Z.: Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst. Appl. 42(21), 7344–7355 (2015)

    CrossRef  Google Scholar 

  33. Silveira, T.D., Kozakevicius, A.D.J., Rodrigues, C.R.: Drowsiness detection for single channel EEG by DWT best m-term approximation. Res. Biomed. Eng. 31, 107–115 (2015)

    CrossRef  Google Scholar 

  34. Tabal, K.M.R., Caluyo, F.S., Ibarra, J.B.G.: Microcontroller-implemented artificial neural network for electrooculography-based wearable drowsiness detection system. In: Sulaiman, H.A., Othman, M.A., Othman, M.F.I., Rahim, Y.A., Pee, N.C. (eds.) Advanced Computer and Communication Engineering Technology. LNEE, vol. 362, pp. 461–472. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24584-3_39

    CrossRef  Google Scholar 

  35. Zhenhai, G., DinhDat, L., Hongyu, H., Ziwen, Y., Xinyu, W.: Driver drowsiness detection based on time series analysis of steering wheel angular velocity. In: 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 99–101. IEEE (2017)

    Google Scholar 

  36. Otmani, S., Pebayle, T., Roge, J., Muzet, A.: Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers. Physiol. Behav. 84, 715–724 (2005)

    CrossRef  Google Scholar 

  37. Liu, C.C., Hosking, S.G., Lenné, M.G.: Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J. Saf. Res. 40, 239–245 (2009)

    CrossRef  Google Scholar 

  38. Ingre, M., Åkerstedt, T., Peters, B., Anund, A., Kecklund, G.: Subjective sleepiness, simulated driving performance and blink duration: examining individual differences. J. Sleep Res. 15, 47–53 (2006)

    CrossRef  Google Scholar 

  39. Królak, A., Strumiłło, P.: Eye-blink detection system for human–computer interaction. Univ. Access Inf. Soc. 11, 409–419 (2012)

    CrossRef  Google Scholar 

  40. 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, 63–77 (2006)

    CrossRef  Google Scholar 

  41. Tang, X., Zhou, P., Wang, P.: Real-time image-based driver fatigue detection and monitoring system for monitoring driver vigilance. In: 2016 35th Chinese on Control Conference (CCC), pp. 4188–4193. IEEE (2016)

    Google Scholar 

  42. Ahmad, R., Borole, J.: Drowsy driver identification using eye blink detection. IJISET-Int. J. Comput. Sci. Inf. Technol. 6, 270–274 (2015)

    Google Scholar 

  43. Yan, J.-J., Kuo, H.-H., Lin, Y.-F., Liao, T.-L.: Real-time driver drowsiness detection system based on PERCLOS and grayscale image processing. In: 2016 International Symposium on Computer, Consumer and Control (IS3C), pp. 243–246. IEEE (2016)

    Google Scholar 

  44. Bhandari, G., Durge, A., Bidwai, A., Aware, U.: Yawning analysis for driver drowsiness detection. Int. J. Eng. Res. Technol. 3, 502–505 (2014)

    Google Scholar 

  45. Tran, D., Tadesse, E., Sheng, W., Sun, Y., Liu, M., Zhang, S.: A driver assistance framework based on driver drowsiness detection. In: 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 173–178. IEEE (2016)

    Google Scholar 

  46. Lee, B.-G., Chung, W.-Y.: Driver alertness monitoring using fusion of facial features and bio-signals. IEEE Sens. J. 12, 2416–2422 (2012)

    CrossRef  Google Scholar 

  47. Nakamura, T., Maejima, A., Morishima, S.: Detection of driver’s drowsy facial expression. In: 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 749–753. IEEE (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Aslam .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ullah, M.R., Aslam, M., Ullah, M.I., Maria, ME.A. (2018). Driver’s Drowsiness Detection Through Computer Vision: A Review. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02840-4_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02839-8

  • Online ISBN: 978-3-030-02840-4

  • eBook Packages: Computer ScienceComputer Science (R0)