GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction
Video recordings of child-caregiver interactions allow study of attentional dynamics, but manual annotation is time-consuming. GBAT is a deep-learning toolkit that automates synchronization, gaze annotation, and pose/hand action categorization, enhancing efficiency for large-scale developmental research.
Article intelligence
Key points
- GBAT automates three key preprocessing steps: post-hoc video synchronization, semi-automatic gaze target annotation, and pose/hand action categorization.
- It reduces manual annotation time for child-caregiver interaction videos.
- The toolkit supports large-scale and longitudinal studies of attentional dynamics in early development.
Why it matters
This matters because GBAT automates three key preprocessing steps: post-hoc video synchronization, semi-automatic gaze target annotation, and pose/hand action categorization.
Technical impact
May affect research directions, evaluation methods, open-source reproduction, and productization paths.
[2605.22962] GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction
[Submitted on 21 May 2026]
Title:GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction
View a PDF of the paper titled GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction, by Iba Baig and 6 other authors
View PDF HTML (experimental)
Abstract:Video recordings of child-caregiver interactions enable investigation of attentional dynamics during naturalistic behavior. Such multimodal recording also allows researchers to examine how attention interacts with action and language use in real time. However, manual annotation of such data is time-consuming. Here, we introduce GazeBehavior Annotation Toolkit, a deep-learning-based toolkit designed to facilitate three key processes in data preprocessing and feature extraction: post-hoc synchronization across multiple videos, semi-automatic annotation of gaze target categories, and categorization of participants' poses and hand actions. This toolkit improves the efficiency and scalability of feature extraction from human egocentric eye-tracking and video data. Such improvement is critical in supporting large-scale and longitudinal investigations of attentional dynamics and naturalistic behavior in human early development.
Comments: submitted to IEEE International Conference on Development and Learning (ICDL), 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE); Neurons and Cognition (q-bio.NC)
Cite as: arXiv:2605.22962 [cs.CV]
(or arXiv:2605.22962v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.22962
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ming Bo Cai [view email] [v1] Thu, 21 May 2026 18:47:56 UTC (1,674 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction, by Iba Baig and 6 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-05
Change to browse by:
cs cs.CE cs.HC cs.SE q-bio q-bio.NC
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)