Driver Safety Approach Using Efficient Image Processing Algorithms for Driver Distraction Detection and Alerting

  • Omar Wathiq
  • Bhavna D. Ambudkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


Currently, due to different reasons, the road accidents are increasing. Road accidents are prone to number human deaths. There are different reasons which lead to road accidents, but drivers fatigue or distraction is main threat in major accidental cases. Therefore, recently various methods are explained by many authors for untimely identification of driver sleepiness in the manner of prohibiting mischance on road. In this paper, we are presenting the novel approach called hybrid method in which automatic care of driver safety and hospitality management services. Our approach aims at determining first if a driver is distracted or not based yawing, eye position, head position, mouth position etc., second if driver is detected as distracted instance alarming will perform on both driver side and near hospital services in order to be available in case of accident happen. Based on computer vision techniques, we propose four different modules for features extraction, focusing on arm position, face orientation, facial expression and eye behaviour, and then, the outputs of all these phases combined together and feed to the classifier feed-forward neural network (FFNN) for alarming the distraction detection and type of distraction. The outcome of this paper is efficient driver safety approach by considering the RGB-D sensor and image processing algorithms.


Feature extraction Driver safety Driver distraction Fatigue Face detection Eyes detection Yawing SVM 


  1. 1.
    Smith, P., Shah, M., Lobo, N.V.: Monitoring head/eye motion for driver alertness with one camera. In: Proceeding of 15th IEEE International Conference on Pattern Recognition, Barcelona, Spain (2000)Google Scholar
  2. 2.
    Tabrizi, P.R., Zoroofi, R.A.: Open/closed eye analysis for drowsiness detection. In: Proceeding of 1st Workshops on Image Processing Theory, Tools and Applications, Sousse, Tunisia, Nov 2008Google Scholar
  3. 3.
    Hamada, T., Ito, T., Adachi, K., Nakano, T., Yamamoto, S.: Detecting method for drivers’ drowsiness applicable to individual features. In: Proceeding of IEEE Intelligent Transportation Systems, Shanghai, China, Oct 2003Google Scholar
  4. 4.
    Smith, P., Shah, M., Lobo, N.V.: Determining driver visual attention with one camera. IEEE Trans. Intell. Transp. Syst. 4(4) (2003)Google Scholar
  5. 5.
    Wang, F., Qin, H.: A FPGA based driver drowsiness detecting system. In: Proceedings of IEEE International Conference on Vehicular Electronics and Safety, Xian, China, Oct 2005Google Scholar
  6. 6.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceeding of International Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA (2001)Google Scholar
  7. 7.
    Zhu, Z., Fujimura, K., Ji, Q.: Real-time eye detection and tracking under various light conditions. In: ACM Eye Tracking Research and Application symposium, New Odeans, LA, USA (2002)Google Scholar
  8. 8.
    Zhao, S., Grigat, R.R.: Robust eye detection under active infrared illumination. In: Proceeding of 18th IEEE International Conference on Pattern Recognition (ICPR), Hong Kong, China, Sept 2006Google Scholar
  9. 9.
    Brandt, T., Stemmer, R., Mertsching, B., Rakotonirainy, A.: Affordable visual driver monitoring system for fatigue and monotony. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Hague, Netherlands, Oct 2004Google Scholar
  10. 10.
    Tabrizi, P.R., Zoroofi, R.A.: Drowsiness detection based on brightness and numeral features of eye image. In: Proceeding of 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kyoto, Japan, Sept 2009Google Scholar
  11. 11.
    Horng, W.B., Chen, C.Y., Chang, Y., Fan, C.H.: Driver fatigue detection based on eye tracking and dynamic template matching. In: Proceeding of IEEE International Conference on Networking, Sensing & Control, Taipei, Taiwan, Mar 2004Google Scholar
  12. 12.
    Batista, J.: A drowsiness and point of attention monitoring system for driver vigilance. In: Proceeding of IEEE Intelligent Transportation Systems Conference, Seattle, USA, Oct 2007Google Scholar
  13. 13.
    Flores, M.J., Armingol, J.M., Escalera, A.: Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions. EURASIP J. Adv. Signal Process. (2010)Google Scholar
  14. 14.
    Hariri, B., Abtahi, S., Shirmohammadi, S., Martel, L.: A yawning measurement method to detect driver drowsinessGoogle Scholar
  15. 15.
    Singh, I., Banga, V.K.: Development of a drowsiness warning system using neural network. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2(8) (2013). (An ISO 3297: 2007 Certified Organization)Google Scholar
  16. 16.
    Craye, C., Karray, F.: Driver distraction detection and recognition using RGB-D sensor. arXiv:1502.00250v1 [cs.CV] 1 Feb 2015
  17. 17.
    Jimenez-Pinto, J., Torres-Torriti, M.: Face salient points and eyes tracking for robust drowsiness detection. Robotica 30(5) (2012)Google Scholar
  18. 18.
    Grace, R., Byme, V.E., Bierman, D.M., Legrand, J.M., Gricourt, D., Davis, R.K., Staszewski, J.J., Carnahan, B.: A drowsy driver detection system for heavy vehicles. In: Proceedings of 17th AIAA/IEEE/SAE Digital Avionics Systems Conference (DASC), Washington, USA, Nov 1998Google Scholar
  19. 19.
    Rang-Ben, W., Ke-You, G., Shu-ming, S., Jiang-wei, C.: A monitoring method of driver fatigue behavior based on machine vision. In: Proceeding of IEEE Intelligent Vehicles Symposium, Columbus, Ohio, USA, June 2003Google Scholar
  20. 20.
    Veeraraghavan, H., Papanikolopoulos, N.: Detecting driver fatigue through the use of advanced face monitoring techniques. In: Intelligent Transportation System Institute, Department of Computer Science and Engineering, University of Minnesota (2001)Google Scholar
  21. 21.
    Dong, W., Wu, X.: Driver fatigue detection based on the distance of eyelid. In: Proceeding of IEEE International Workshop VLSI Design & Video Technology, Suzhou, China, May 2005Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.(E&TC)Dr. D.Y. Patil Institute of Engineering & TechnologyPuneIndia

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