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; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 21-30

Abstract


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.

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[bibtex]
@InProceedings{Hatamimajoumerd_2024_WACV, author = {Hatamimajoumerd, Elaheh and Kakhaki, Pooria Daneshvar and Huang, Xiaofei and Luan, Lingfei and Amraee, Somaieh and Ostadabbas, Sarah}, title = {Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops}, month = {January}, year = {2024}, pages = {21-30} }