ArticleInfancy : the official journal of the International Society on Infant Studies2022
iCatcher: A neural network approach for automated coding of young children's eye movements.
Article in Infancy : the official journal of the International Society on Infant Studies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Comparing automated gaze classifiers in infant looking studies: Accuracy and vulnerability to environmental factors.Behavior research methods · 2026Article
- The ADVANCE toolkit: Automated descriptive video annotation in naturalistic child environments.Behavior research methods · 2025Article
- Automated iPad-based gaze detection in the NIH Baby Toolbox® norming study.Infant behavior & development · 2025Article
- From focus to function: Longitudinal insights into infant attention and emerging executive functions via remote webcam eye tracking.Developmental psychology · 2025Article
- Catching up with iCatcher: Comparing analyses of infant eye tracking based on trained human coders and iCatcher+ automated gaze coding software.Behavior research methods · 2025Article
- An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals.Scientific reports · 2025Article
- The Feasibility of Remote Visual-World Eye-Tracking With Young Children.Open mind : discoveries in cognitive science · 2025Article
- Exploration of factors affecting webcam-based automated gaze coding.Behavior research methods · 2024Article
- Conducting Developmental Research Online vs. In-Person: A Meta-Analysis.Open mind : discoveries in cognitive science · 2024Article
- OWLET: An automated, open-source method for infant gaze tracking using smartphone and webcam recordings.Behavior research methods · 2023Article
- iCatcher: A neural network approach for automated coding of young children's eye movements.Infancy : the official journal of the International Society on Infant Studies · 2022Article
- iCatcher+: Robust and Automated Annotation of Infants' and Young Children's Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies.Advances in methods and practices in psychological scienceArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Infants' looking behaviors are often used for measuring attention, real-time processing, and learning-often using low-resolution videos. Despite the ubiquity of gaze-related methods in developmental science, current analysis techniques usually involve laborious post hoc coding, imprecise real-time coding, or expensive eye trackers that may increase data loss and require a calibration phase. As an alternative, we propose using computer vision methods to perform automatic gaze estimation from low-resolution videos. At the core of our approach is a neural network that classifies gaze directions in real time. We compared our method, called iCatcher, to manually annotated videos from a prior study in which infants looked at one of two pictures on a screen. We demonstrated that the accuracy of iCatcher approximates that of human annotators and that it replicates the prior study's results. Our method is publicly available as an open-source repository at https://github.com/yoterel/iCatcher.
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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.