A Framework for Robust Driver Gaze Classification

2016-01-1426

04/05/2016

Event
SAE 2016 World Congress and Exhibition
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-1426
Pages
8
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.
Additional Details
Publisher
Published
Apr 5, 2016
Product Code
2016-01-1426
Content Type
Technical Paper
Language
English