GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917522561826816 |
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| author | Baig, Iba Li, Kevin Xu, Yanbin Cattelain, Seiji Hallo, Marie Ono, Hayato Tsuji, Sho Cai, Ming Bo |
| author_facet | Baig, Iba Li, Kevin Xu, Yanbin Cattelain, Seiji Hallo, Marie Ono, Hayato Tsuji, Sho Cai, Ming Bo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22962 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction Baig, Iba Li, Kevin Xu, Yanbin Cattelain, Seiji Hallo, Marie Ono, Hayato Tsuji, Sho Cai, Ming Bo Computer Vision and Pattern Recognition Computational Engineering, Finance, and Science Human-Computer Interaction Software Engineering Neurons and Cognition 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. |
| title | GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction |
| topic | Computer Vision and Pattern Recognition Computational Engineering, Finance, and Science Human-Computer Interaction Software Engineering Neurons and Cognition |
| url | https://arxiv.org/abs/2605.22962 |