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Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art

arXiv 2311.12300 ↗First posted 21 Nov 2023cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 21 Nov 2023.

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17 of 30 references.

References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  3. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  4. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  5. 2021Revisiting Skeleton-based Action Recognitioncited by 257
  6. 2014Cross-view Action Modeling, Learning and Recognitioncited by 134
  7. 2017Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networkscited by 62
  8. 2016Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networkscited by 32
  9. 2022A Survey on Video Action Recognition in Sports: Datasets, Methods and Applicationscited by 28
  10. 2020Invariant Representation Learning for Infant Pose Estimation with Small Datacited by 20
  11. 2017Skeleton-based Action Recognition Using LSTM and CNNcited by 10
  12. 2021InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wildcited by 9
5 more
  1. 2022Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotationscited by 4
  2. 2021Heuristic Weakly Supervised 3D Human Pose Estimationcited by 3
  3. 2023A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infantscited by 3
  4. 2019Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Videocited by 2
  5. 2022BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applicationscited by 2

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