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Star-Shaped Multi-Person Interaction Graph Model for Group Skeleton-Based Action Recognition.

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    This study introduces a novel star-shaped model for multi-person skeleton-based action recognition. It accurately identifies the core person and models group interactions for improved performance.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Skeleton-based human action recognition is a growing field.
    • Existing methods struggle with multi-person scenarios and identifying a core person.

    Purpose of the Study:

    • To propose a star-shaped group interaction model for skeleton-based action recognition.
    • To accurately identify and model the core person within group interactions.

    Main Methods:

    • Developed a character importance scoring system considering individual and group aspects.
    • Constructed a core-star interaction graph with differentiated data augmentation.
    • Utilized a star-shaped spatio-temporal graph convolutional network for feature extraction.

    Main Results:

    • Achieved state-of-the-art performance on public benchmark datasets.
    • Obtained accuracies of 79.1% (NBA), 96.1% (Volleyball), and 93.1% (Volleyball-weak).

    Conclusions:

    • The proposed star-shaped model effectively handles complex group actions.
    • Accurate core person identification and interaction modeling enhance action recognition accuracy.