Browse Publications Technical Papers 2016-01-1426
2016-04-05

A Framework for Robust Driver Gaze Classification 2016-01-1426

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.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Members save up to 18% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:
TECHNICAL PAPER

Driver's Lateral Control Strategy as Affected by Task Demands and Driving Experience

770876

View Details

JOURNAL ARTICLE

Collision Prevention While Driving in Real Traffic Flow Using Emotional Learning Fuzzy Inference Systems

2013-01-0623

View Details

TECHNICAL PAPER

Non-Intrusive Driver Drowsiness Monitoring Via Artificial Neural Networks

2008-01-0187

View Details

X