A Framework for Robust Driver Gaze Classification

Paper #:
  • 2016-01-1426

Published:
  • 2016-04-05
DOI:
  • 10.4271/2016-01-1426
Citation:
Fridman, L., Lee, J., Reimer, B., and Mehler, B., "A Framework for Robust Driver Gaze Classification," SAE Technical Paper 2016-01-1426, 2016, https://doi.org/10.4271/2016-01-1426.
Pages:
8
Abstract:
The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.
Access
Now
SAE MOBILUS Subscriber? You may already have access.
Buy
Select
Price
List
Download
$27.00
Mail
$27.00
Members save up to 40% off list price.
Share
HTML for Linking to Page
Page URL

Related Items

Technical Paper / Journal Article
2011-04-12
Article
2016-11-15
Book
2002-08-23