ArticleSensors (Basel, Switzerland)2021
Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 33 citations in OpenAlex.
- eyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification.Journal of eye movement research · 2025Article
- I-MPN: inductive message passing network for efficient human-in-the-loop annotation of mobile eye tracking data.Scientific reports · 2025Article
- Towards Automatic Object Detection and Activity Recognition in Indoor Climbing.Sensors (Basel, Switzerland) · 2024Article
- MYFix: Automated Fixation Annotation of Eye-Tracking Videos.Sensors (Basel, Switzerland) · 2024Article
- A review of machine learning in scanpath analysis for passive gaze-based interaction.Frontiers in artificial intelligence · 2024Review
- Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification.Frontiers in physiology · 2024Article
- Quantifying Dwell Time With Location-based Augmented Reality: Dynamic AOI Analysis on Mobile Eye Tracking Data With Vision Transformer.Journal of eye movement research · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. These stimuli, which are prevalent subjects of diagnostic eye tracking studies, are commonly encoded as rectangular areas of interest (AOIs) per frame. Because it is a tedious manual annotation task, the automatic detection and annotation of visual attention to AOIs can accelerate and objectify eye tracking research, in particular for mobile eye tracking with egocentric video feeds. In this work, we implement two methods to automatically detect visual attention to AOIs using pre-trained deep learning models for image classification and object detection. Furthermore, we develop an evaluation framework based on the VISUS dataset and well-known performance metrics from the field of activity recognition. We systematically evaluate our methods within this framework, discuss potentials and limitations, and propose ways to improve the performance of future automatic visual attention detection methods.
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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.