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Related Experiment Video

Updated: Nov 9, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

A single-camera gaze tracking system under natural light.

Feng Xiao¹, Dandan Zheng¹, Kejie Huang¹

¹Institute of VLSI Design, Zhejiang University, China.

Journal of Eye Movement Research

|April 8, 2021

Summary

This summary is machine-generated.

This study introduces a versatile single-camera gaze tracking system for improved human-computer interaction. The novel method enhances accuracy using stable facial landmarks and eye vectors, outperforming existing techniques.

Keywords:

Eye movement anchor point eye tracking facial landmark gaze head pose iris center mapping functions single-camera usability

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Last Updated: Nov 9, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

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Published on: November 14, 2018

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Area of Science:

Computer Vision
Human-Computer Interaction
Biomedical Engineering

Background:

Gaze tracking is a key human-computer interaction technology with broad academic and industrial interest.
Current systems face limitations in popularization due to sensor and algorithm performance constraints.
A need exists for accessible and versatile gaze tracking solutions adaptable to natural environments.

Purpose of the Study:

To propose and evaluate a novel single-camera gaze tracking system operating under natural light.
To enhance the accuracy and versatility of gaze tracking for broader adoption.
To improve upon existing gaze tracking methodologies through innovative feature detection and mapping.

Main Methods:

Utilized a single camera for gaze tracking under natural lighting conditions.
Employed a simple active contour model (snakuscule) for accurate iris center detection, initialized with eye anatomical priors.
Computed a novel anchor point using stable facial landmarks.
Applied second-order mapping functions incorporating eye vectors and head pose for point of regard estimation.
Implemented a weight coefficient on eye points of regard to minimize gaze errors.

Main Results:

Achieved 98.87% accuracy for iris center feature position on the GI4E database with a normalized error below 0.05.
Demonstrated superior performance compared to state-of-the-art appearance-based and feature-based gaze tracking methods on the EYEDIAP database.
The proposed system shows high accuracy and robustness in naturalistic settings.

Conclusions:

The developed single-camera system offers a versatile and accurate solution for gaze tracking.
The novel approach to anchor point computation and error minimization significantly improves tracking precision.
This research paves the way for more widespread and accessible gaze tracking applications.