Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The effect of unilateral cortical blindness on lane position and gaze behavior in a virtual reality steering task.

Scientific reportsĀ·2026
Same author

Multisensory stimuli facilitate low-level perceptual learning on a diļ¬ƒcult global motion task in virtual reality.

PloS oneĀ·2025
Same author

The effect of unilateral cortical blindness on lane position and gaze behavior in a virtual reality steering task.

bioRxiv : the preprint server for biologyĀ·2025
Same author

Optic flow density modulates corner-cutting in a virtual steering task for younger and older adults.

Scientific reportsĀ·2024
Same author

Coordination of gaze and action during high-speed steering and obstacle avoidance.

PloS oneĀ·2024
Same author

A neural active inference model of perceptual-motor learning.

Frontiers in computational neuroscienceĀ·2023
Same journal

ManiLoco: A VR-Based Locomotion Method for Concurrent Object Manipulation.

Proceedings of the ACM on computer graphics and interactive techniquesĀ·2023
Same journal

Rethinking Model-Based Gaze Estimation.

Proceedings of the ACM on computer graphics and interactive techniquesĀ·2022
See all related articles

Related Experiment Video

Updated: May 30, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K

Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality.

Kevin Barkevich1, Reynold Bailey1, Gabriel J Diaz1

  • 1Rochester Institute of Technology, USA.

Proceedings of the ACM on Computer Graphics and Interactive Techniques
|August 9, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning eye tracking improves pupil tracking but may impact gaze estimation accuracy. This study objectively assesses ML methods

Keywords:
eye trackinggaze estimationneural networksvirtual reality

More Related Videos

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

906
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

920

Related Experiment Videos

Last Updated: May 30, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K
Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

906
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

920

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Gaze estimation algorithms rely on tracking eye features like the pupil.
  • Traditional computer vision methods struggle with occlusion and reflections.
  • Machine learning (ML) shows promise for improved pupil tracking.

Purpose of the Study:

  • To objectively assess the impact of ML-based pupil tracking on gaze estimation quality.
  • To compare ML methods against traditional techniques in gaze estimation.
  • To evaluate accuracy, precision, and dropout rates of gaze estimates.

Main Methods:

  • Implemented and evaluated several contemporary ML-based eye tracking methods.
  • Assessed the impact of ML tracking on both feature-based and model-based gaze estimation.
  • Measured gaze estimation accuracy, precision, and dropout rate.

Main Results:

  • ML-based pupil tracking can influence the final gaze estimate quality.
  • Objective metrics reveal performance variations depending on the ML method and gaze estimation approach.
  • Comparison highlights trade-offs between segmentation performance and gaze accuracy.

Conclusions:

  • ML methods offer advancements in pupil tracking for eye trackers.
  • Careful evaluation is needed to understand the downstream effects of ML on gaze estimation.
  • This research provides critical insights for developing robust eye tracking systems.