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.

翻译：儿童-看护者互动的视频记录使我们能够在自然行为中研究注意动态。这种多模态记录还允许研究人员实时考察注意力如何与行动和语言使用相互作用。然而，对此类数据进行人工注释非常耗时。本文介绍了注视行为注释工具包（GazeBehavior Annotation Toolkit），这是一个基于深度学习的工具包，旨在促进数据预处理和特征提取中的三个关键过程：多个视频的事后同步、注视目标类别的半自动注释以及参与者姿态和手部动作的分类。该工具提高了从人类自我中心眼动追踪和视频数据中提取特征的效率和可扩展性。这一改进对于支持人类早期发展中注意动态和自然行为的大规模纵向研究至关重要。