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
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Bibliography
T.F. Cootes, G.J. Edwards, C.J. Taylor, Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23, 681–685 (2001)
D. Dementhon, L. Davis, Model-based object pose in 25 lines of code. Int. J. Comput. Vis. 15, 123–141 (1995)
Fermat–Torricelli problem, Encyclopedia of Mathematics (2014), www.encyclopediaofmath.org/index.php/Fermat-Torricelli_problem
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. 499–506
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. 128–131
V. Krüger, G. Sommer, Gabor wavelet networks for efficient head pose estimation. Image Vis. Comput. 20, 665–672 (2002)
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)
P. Martins, J. Batista, Monocular head pose estimation, in Proceedings of International Conference on Image Analysis Recognition (2008), pp. 357–368
MUCT Face Dataset (2014), www.milbo.org/muct/
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)
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. 302–313
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)
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. 356–361
TurboSquid (2013), www.turbosquid.com/Search/3D-Models/face
Visage Tech (2014), www.visagetechnologies.com/
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. 2879–2886
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. 535–542
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Rezaei, M., Klette, R. (2017). Driver Inattention Detection. In: Computer Vision for Driver Assistance. Computational Imaging and Vision, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-50551-0_6
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DOI: https://doi.org/10.1007/978-3-319-50551-0_6
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