Latest revision as of 06:36, 2 February 2021

Abstract

Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.

Original document

The different versions of the original document can be found in:

• http://cvrr.ucsd.edu/publications/2007/MurphyChutorian_Trivedi_ITSC07.pdf

• http://xplorestaging.ieee.org/ielx5/4357618/4357619/04357803.pdf?arnumber=4357803,

http://dx.doi.org/10.1109/itsc.2007.4357803

• https://dblp.uni-trier.de/db/conf/itsc/itsc2007.html#Murphy-Chutorian07,

https://cvrr.ucsd.edu/publications/2007/MurphyChutorian_Trivedi_ITSC07.pdf,

http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000004357803,

https://ieeexplore.ieee.org/document/4357803,

https://academic.microsoft.com/#/detail/2142727917