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A Systematic Review of Skeleton-Based Action Recognition: Methods, Challenges, and Future Directions.

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    This summary is machine-generated.

    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.

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    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.