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A Systematic Review of Skeleton-Based Action Recognition: Methods, Challenges, and Future Directions
IEEE Transactions on Neural Networks and Learning Systems
|December 8, 2025
Summary
This study reviews skeleton-based human action recognition (HAR), addressing challenges in data reliance, few-shot learning, and representation. It provides insights into current methods and datasets for advancing HAR research.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Human Action Recognition (HAR) is a key computer vision research area.
- Skeleton data offers advantages over other modalities for HAR due to efficient semantics and motion patterns.
- Existing HAR methods face challenges with labeled data dependency, few-shot learning, and single-modality representation.
Purpose of the Study:
- To comprehensively review existing skeleton-based action recognition methods.
- To analyze publicly available action recognition datasets.
- To stimulate innovative ideas and promote breakthroughs in skeleton-based HAR.
Main Methods:
- Literature review of skeleton-based action recognition techniques.
- Analysis of current challenges in the field.
- Review and analysis of action recognition datasets.
Main Results:
- Identified key challenges in skeleton-based HAR: reducing labeled data reliance, few-shot learning, and improving spatio-temporal representation.
- Provided a comprehensive overview of existing methods and their limitations.
- Cataloged and analyzed relevant public datasets for HAR research.
Conclusions:
- Skeleton-based HAR is a rapidly advancing field with significant potential.
- Addressing current challenges is crucial for broader applications.
- This review offers a valuable perspective for future research and development in HAR.
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