Vision-Based Driver’s Attention Monitoring System for Smart Vehicles

  • Lamia Alam
  • Mohammed Moshiul HoqueEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 866)


Recent studies revealed that the driver’s inattention is one of the most prominent reasons for car accidents. Intelligent driving assistant system with real time monitoring of the driver’s attentional status may reduce the accident rate that mostly occurred due to lack of attention. In this paper, we presents a vision-based driver’s attention monitoring system that estimates the driver’s attentional status in terms of four categories: attentive, distracted, drowsy, and fatigue respectively. The attentional status is classified with a variety of parameters such as, percentage of eyelid closure over time (PERCLOS), yawn frequency and gaze direction. Experimental results with different subjects show that the system can classify the driver’s attentional status with a reasonable accuracy.


Computer vision Human computer interaction Attentional status Yawn frequency Gaze direction 


  1. 1.
    Global Status Report on Road Safety 2015, World Health Organization, WHO Press, Switzerland.
  2. 2.
    Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., Ramsey, D.J.: The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data. Technical report, National Highway Traffic Safety Administration, Washington, DC, USA (2006)Google Scholar
  3. 3.
    Arun, S., Sundaraj, K., Murugappan, M.: Driver inattention detection methods: a review. In: IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT), pp. 1–6, Kuala Lumpur (2012)Google Scholar
  4. 4.
    De Valck, E., Cluydts, R.: Slow-release caffeine as a countermeasure to driver sleepiness induced by partial sleep deprivation. J. Sleep Res. 10, 203–209 (2001)CrossRefGoogle Scholar
  5. 5.
    Guo, Z., Pan, Y., Zhao, G., Cao, S., Zhang, J.: Detection of driver vigilance level using EEG signals and driving contexts. IEEE Trans. Reliab. 67(1), 370–380 (2018)CrossRefGoogle Scholar
  6. 6.
    Wang, H., Dragomir, A., Abbasi, N.I., Li, J., Thakor, N.V., Bezerianos, A.: A novel real-time driving fatigue detection system based on wireless dry EEG. Cogn. Neurodynamics, 1–12. Springer, Netherlands (2018)Google Scholar
  7. 7.
    Li, G., Chung, W.Y.: Combined EEG-Gyroscope-tDCS brain machine interface system for early management of driver drowsiness. IEEE Trans. Hum.-Mach. Syst. 48(1), 50–62 (2018)CrossRefGoogle Scholar
  8. 8.
    Schoiack, M. M. V.: Driver drowsiness detection and verification system and method. U.S. Patent 8,631,893 B2 (2014)Google Scholar
  9. 9.
    Shibli, A. M., Hoque, M. M., Alam, L.: Developing a vision-based driving assistance system. In: 2018 International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS), Kolkata, India (2018)Google Scholar
  10. 10.
    Chien,J.-C., Chen, Y.-S., Lee, J.-D.: Improving night time driving safety using vision-based classification techniques. Sensors 17(10), 2199 (2017)CrossRefGoogle Scholar
  11. 11.
    Mandal, B., Li, L., Wang, G.S., Lin, J.: Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans. Intell. Transp. Syst. 18(3), 545–557 (2017)CrossRefGoogle Scholar
  12. 12.
    Chowdhury, P., Alam, L., Hoque, M. M.: Designing an empirical framework to estimate the driver’s attention. In: 5th International Conference on Informatics, Electronics & Vision (ICIEV), pp. 513–518, Dhaka, Bangladesh (2016)Google Scholar
  13. 13.
    Vicente, F., Huang, Z., Xiong, X., Torre, F.D.I., Zhang, W., Levi, D.: Driver gaze tracking and eyes off the road detection system. IEEE Trans. Intell. Transp. Syst. 16(4), 2014–2027 (2015)CrossRefGoogle Scholar
  14. 14.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Computational Learning Theory, pp. 23–37. Springer, Heidelberg (1995)Google Scholar
  15. 15.
    De, K.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)Google Scholar
  16. 16.
    Martin, D., Häger, G., Khan, F.H., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)Google Scholar
  17. 17.
  18. 18.
    Soukupova, T., Cech, J.: Real-time eye blink detection using facial landmarks. In: Cehovin, L., Mandeljc, R., Struc, V. (eds.) 21st Computer Vision Winter Workshop. Rimske Toplice, Slovenia (2016)Google Scholar
  19. 19.
    Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985)CrossRefGoogle Scholar
  20. 20.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8, 179–187 (1962)zbMATHGoogle Scholar
  21. 21.
    Arbuck, D.: Is yawning a tool for wakefulness or for sleep? Open J. Psychiatry 3(1), 5–11 (2013)CrossRefGoogle Scholar
  22. 22.
    Chang, F.-J., Tran, A.T., Hassner, T., Masi, I., Nevatia, R., Medioni, G.: FacePoseNet: making a case for landmark-free face alignment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1599–1608, Venice, Italy (2017)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringChittagong University of Engineering & TechnologyChittagongBangladesh

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