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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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Quantifying Dwell Time With Location-based Augmented Reality: Dynamic AOI Analysis on Mobile Eye Tracking Data With

Julien Mercier^1,2, Olivier Ertz¹, Erwan Bocher²

¹MEI, School of Engineering and Management Vaud, HES-SO, Switzerland.

Journal of Eye Movement Research

|June 12, 2024

Summary

Researchers developed a Vision Transformer (ViT) model to accurately analyze mobile eye-tracking data from naturalistic studies. This automated method overcomes limitations of manual annotation for moving targets, improving data analysis efficiency.

Keywords:

Dwell Time Dynamic Area of Interest Educational Technology Frame-byframe analysis Location-based Augmented Reality Mobile Eye Tracking Methodology Vision Transformer

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Area of Science:

Computer Vision
Human-Computer Interaction
Cognitive Science

Background:

Mobile eye tracking offers valuable egocentric vision data for naturalistic studies but suffers from noise and analysis challenges with moving targets.
Current analysis tools are limited, often necessitating time-consuming manual annotation, hindering the scalability of mobile eye-tracking research.
Nonlinear movement and object disappearances in outdoor settings complicate automated area of interest analysis.

Purpose of the Study:

To introduce an automated method for analyzing mobile eye-tracking data, specifically addressing challenges with moving targets in naturalistic environments.
To improve the efficiency and accuracy of processing noisy eye-tracking data from outdoor studies.
To enable more scalable and less labor-intensive analysis of mobile eye-tracking data.

Main Methods:

A fine-tuned Vision Transformer (ViT) model was developed for classifying frames containing gaze markers.
The ViT model was trained on a manually labeled subset (1.98%) of the entire dataset over three epochs.
The model's performance was evaluated using hold-out data to assess its accuracy.

Main Results:

The fine-tuned Vision Transformer model achieved a high accuracy of 99.34% on hold-out data.
The method was successfully applied to quantify participant dwell time on a tablet during an outdoor augmented reality biodiversity education application test.
Demonstrated the model's capability to handle complex, naturalistic scenarios with moving elements.

Conclusions:

The developed Vision Transformer-based method offers an accurate and efficient solution for analyzing mobile eye-tracking data in naturalistic studies.
This approach significantly reduces reliance on manual annotation, paving the way for broader application of mobile eye tracking.
The method shows potential for application in diverse research areas requiring analysis of gaze behavior in dynamic environments.