Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art
This addresses the need for automated infant action recognition for applications like safety monitoring and developmental tracking, but it is incremental as it primarily benchmarks existing methods on new data.
The paper tackles the problem of infant action recognition by introducing a new dataset, InfActPrimitive, and evaluating state-of-the-art skeleton-based models, finding that PoseC3D achieves the highest accuracy at about 71% while others struggle, revealing a significant gap compared to adult action recognition.
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.