用AI自动标注儿童与照护者互动中的视线与行为，提升研究效率。
GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction
- 基于深度学习，自动同步多视频并标注视线目标类别
- 支持姿势与手部动作分类，实现半自动标注流程
- 适合从事婴幼儿注意力发展研究的团队使用
儿童与照护者互动的视频记录可揭示自然行为中注意力动态。此类多模态数据还能实时分析注意力如何与动作和语言交互。然而，手动标注耗时费力。本文提出GazeBehavior Annotation Toolkit（GBAT），一个基于深度学习的工具包，用于加速三个关键步骤：多视频事后同步、视线目标类别的半自动标注，以及参与者姿势与手部动作的分类。该工具显著提升人眼视角眼动与视频数据特征提取的效率与可扩展性，对大规模、长期追踪人类早期发展中注意力动态的研究具有重要意义。
原文摘要 · Abstract (English)
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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