GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction
Quick Take
The GazeBehavior Annotation Toolkit (GBAT) utilizes deep learning to automate the annotation of egocentric eye-tracking and video data in child-caregiver interactions, enhancing efficiency in data preprocessing and feature extraction. This toolkit supports large-scale studies of attentional dynamics and naturalistic behavior in early childhood development.
Key Points
- GBAT automates post-hoc synchronization of multiple video recordings.
- It offers semi-automatic annotation of gaze target categories.
- The toolkit categorizes participants' poses and hand actions.
- GBAT enhances scalability for longitudinal studies in child development.
- It significantly reduces the time required for manual data annotation.
Article Excerpt
From source RSS / original summaryarXiv:2605. 22962v1 Announce Type: new 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.
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