Challenges in Video-Based Infant Action Recognition: A Critical
  Examination of the State of the Art
Elaheh Hatamimajoumerd Pooria Daneshvar Kakhaki Xiaofei Huang Lingfei Luan Somaieh Amraee Sarah Ostadabbas
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.