Driver Inattention Detection

  • Mahdi Rezaei
  • Reinhard Klette
Chapter
Part of the Computational Imaging and Vision book series (CIVI, volume 45)

Abstract

This chapter mainly proposes a comprehensive method for detecting driver’s distraction and inattention. We introduce an asymmetric appearance-modelling method and an accurate 2D-to-3D registration technique to obtain the driver’s head pose, yawing detection, and head-nodding detection. Chapter  5 and this chapter present the first major objective of this book’s focus on “driver behaviour” (i.e. driver drowsiness and distraction detection). The final objective of this book is to develop an ADAS that correlates driver’s direction of attention to the road hazards, by analyzing both simultaneously. This is presented in Chap.  8

Bibliography

  1. 35.
    T.F. Cootes, G.J. Edwards, C.J. Taylor, Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23, 681–685 (2001)CrossRefGoogle Scholar
  2. 45.
    D. Dementhon, L. Davis, Model-based object pose in 25 lines of code. Int. J. Comput. Vis. 15, 123–141 (1995)CrossRefGoogle Scholar
  3. 65.
    Fermat–Torricelli problem, Encyclopedia of Mathematics (2014), www.encyclopediaofmath.org/index.php/Fermat-Torricelli_problem
  4. 77.
    T. Gernoth, K. Martinez, A. Gooben, R. Grigat, Facial pose estimation using active appearance models and a generic face model, in Proceedings of the IEEE International Conference on Computer Vision Theory and Applications (2010), pp. 499–506Google Scholar
  5. 102.
    X. Jian-Feng, X. Mei, Z. Wei, Driver fatigue detection based on head gesture and PERCLOS, in Proceedings of the Wavelet Active Media Technology and Information Processing (2012), pp. 128–131Google Scholar
  6. 126.
    V. Krüger, G. Sommer, Gabor wavelet networks for efficient head pose estimation. Image Vis. Comput. 20, 665–672 (2002)CrossRefGoogle Scholar
  7. 131.
    V. Lepetit, F. Moreno-Noguer, P. Fua, EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81, 155–166 (2009)CrossRefGoogle Scholar
  8. 153.
    P. Martins, J. Batista, Monocular head pose estimation, in Proceedings of International Conference on Image Analysis Recognition (2008), pp. 357–368Google Scholar
  9. 172.
    MUCT Face Dataset (2014), www.milbo.org/muct/ Google Scholar
  10. 175.
    E. Murphy-Chutorian, M.M. Trivedi, Head pose estimation and augmented reality tracking: an integrated system and evaluation for monitoring driver awareness. IEEE Trans. Intell. Transp. Syst. 11, 300–311 (2010)CrossRefGoogle Scholar
  11. 216.
    M. Rezaei, H. Ziaei Nafchi, S. Morales, Global Haar-like features: a new extension of classic Haar features for efficient face detection in noisy images, in Proceedings of Pacific-Rim Symposium on Image Video Technology. LNCS 8333 (2013), pp. 302–313Google Scholar
  12. 238.
    J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake, Real-time human pose recognition in parts from single depth images. Stud. Comput. Intell. 411, 119–135 (2013)Google Scholar
  13. 254.
    L. Teijeiro-Mosquera, J.L. Alba-Castro, Recursive pose dependent AAM: application to drivers’ monitoring, in Proceedings of the IEEE Intelligent Vehicles Symposium (2011), pp. 356–361Google Scholar
  14. 264.
  15. 273.
    Visage Tech (2014), www.visagetechnologies.com/
  16. 294.
    Z. Xiangxin, D. Ramanan, Face detection, pose estimation, and landmark localization in the wild, in Proceedings of the IEEE Computer Vision Pattern Recognition (2012), pp. 2879–2886Google Scholar
  17. 295.
    J. Xiao, S. Baker, I. Matthews, T. Kanade, Real-time combined 2D+3D active appearance models, in Proceedings of the IEEE Computer Vision Pattern Recognition (2004), pp. 535–542Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Reinhard Klette
    • 2
  1. 1.Department of Computer EngineeringQazvin Islamic Azad UniversityQazvinIran
  2. 2.Department of Electrical and Electronic EngineeringAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations