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Related Experiment Video

Updated: May 17, 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

Joint attention by gaze interpolation and saliency.

Zeynep Yücel1, Albert Ali Salah, Çetin Meriçli

  • 1Intelligent Robotics and Communication Laboratories, Advanced Telecommunications Research Institute International, Kyoto 619-0288, Japan. zeynep@atr.jp

IEEE Transactions on Cybernetics
|October 11, 2012
PubMed
Summary

This study introduces an image-based method for robots to understand human gaze, crucial for effective human-robot interaction. The technique accurately estimates gaze direction even in challenging conditions, enabling better joint attention.

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Area of Science:

  • Robotics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Joint attention is vital for effective human-robot interaction.
  • Accurate gaze estimation is challenging due to image acquisition limitations.

Purpose of the Study:

  • To develop an image-based method for establishing joint attention between humans and robots.
  • To investigate gaze direction interpolation from head pose using regression models.

Main Methods:

  • Utilized regression-based interpolation (Gaussian process regression, neural networks) to estimate gaze direction from head pose.
  • Combined interpolated gaze with image-based saliency for improved target point estimation.
  • Tested three saliency schemes in a human-robot interaction scenario.

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Related Experiment Videos

Last Updated: May 17, 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

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

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

Published on: November 14, 2018

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

Main Results:

  • The proposed method successfully establishes joint attention in human-robot interactions.
  • Demonstrated generalization across subjects and robustness under adverse conditions (low light, motion blur).
  • Achieved rapid gaze estimation for effective joint attention.

Conclusions:

  • The image-based gaze interpolation method is effective for establishing joint attention.
  • The approach is robust and generalizes well, even with imperfect image data.
  • This work advances human-robot interaction capabilities through improved gaze understanding.