Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis. Precise action recognition in infants serves a multitude of pivotal purposes, encompassing safety monitoring, developmental milestone tracking, early intervention for developmental delays, fostering parent-infant bonds, advancing computer-aided diagnostics, and contributing to the scientific comprehension of child development. This paper delves into the intricacies of infant action recognition, a domain that has remained relatively uncharted despite the accomplishments in adult action recognition. In this study, we introduce a groundbreaking dataset called ``InfActPrimitive'', encompassing five significant infant milestone action categories, and we incorporate specialized preprocessing for infant data. We conducted an extensive comparative analysis employing cutting-edge skeleton-based action recognition models using this dataset. Our findings reveal that, although the PoseC3D model achieves the highest accuracy at approximately 71%, the remaining models struggle to accurately capture the dynamics of infant actions. This highlights a substantial knowledge gap between infant and adult action recognition domains and the urgent need for data-efficient pipeline models.

该研究针对婴儿动作识别领域展开研究，介绍了一个名为“InfActPrimitive”的重要婴儿里程碑动作数据集，并采用专门的预处理方法对婴儿数据进行分析。研究结果显示，虽然PoseC3D模型在准确率上达到了约71%，但其他模型难以准确捕捉到婴儿动作的动态特征，这凸显了婴儿动作识别领域与成人动作识别领域之间存在知识差距，并迫切需要数据高效的流程模型。

基于视频的婴儿动作识别的挑战：对现有技术的关键审查