ArticleJournal of eye movement research2024
Quantifying Dwell Time With Location-based Augmented Reality: Dynamic AOI Analysis on Mobile Eye Tracking Data With Vision Transformer.
Article in Journal of eye movement research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- How Source Attribution Visualization Shapes User Attention and Preference: An Eye-Tracking Study of Four AI Chatbot Layouts.Journal of eye movement research · 2026Article
- GazePlotter: An open-source solution for the automatic generation of scarf plots from eye-tracking data.Behavior research methods · 2026Article
- The fundamentals of eye tracking part 6: Working with areas of interest.Behavior research methods · 2026Article
- Vision Transformer attention alignment with human visual perception in aesthetic object evaluation.PloS one · 2026Article
- Designing a test battery for real-world visual search.Scientific reports · 2025Article
- Towards Automatic Object Detection and Activity Recognition in Indoor Climbing.Sensors (Basel, Switzerland) · 2024Article
- Understanding consumer perception and acceptance of AI art through eye tracking and Bidirectional Encoder Representations from Transformers-based sentiment analysis.Journal of eye movement research · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Mobile eye tracking captures egocentric vision and is well-suited for naturalistic studies. However, its data is noisy, especially when acquired outdoor with multiple participants over several sessions. Area of interest analysis on moving targets is difficult because A) camera and objects move nonlinearly and may disappear/reappear from the scene; and B) off-the-shelf analysis tools are limited to linearly moving objects. As a result, researchers resort to time-consuming manual annotation, which limits the use of mobile eye tracking in naturalistic studies. We introduce a method based on a fine-tuned Vision Transformer (ViT) model for classifying frames with overlaying gaze markers. After fine-tuning a model on a manually labelled training set made of 1.98% (=7845 frames) of our entire data for three epochs, our model reached 99.34% accuracy as evaluated on hold-out data. We used the method to quantify participants' dwell time on a tablet during the outdoor user test of a mobile augmented reality application for biodiversity education. We discuss the benefits and limitations of our approach and its potential to be applied to other contexts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.