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InfoGCN++: Learning Representation by Predicting the Future for Online Skeleton-Based Action Recognition.

Seunggeun Chi, Hyung-Gun Chi, Qixing Huang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 26, 2024
    PubMed
    Summary
    This summary is machine-generated.

    InfoGCN++ enables real-time skeleton-based action recognition by learning from current and future movements, overcoming InfoGCN

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Skeleton-based action recognition models like InfoGCN achieve high accuracy but require complete action observation.
    • This limitation restricts their use in real-time applications such as surveillance and robotics.
    • Existing methods struggle with online action recognition due to the need for full sequence data.

    Purpose of the Study:

    • To introduce InfoGCN++, an extension of InfoGCN for online skeleton-based action recognition.
    • To enable real-time action classification irrespective of observation sequence length.
    • To develop a model that learns from current and anticipates future movements for robust action representation.

    Main Methods:

    • InfoGCN++ extends the InfoGCN architecture for online action recognition.
    • It treats prediction as an extrapolation problem based on observed actions.
    • Incorporates Neural Ordinary Differential Equations (NODEs) to model continuous evolution of hidden states.

    Main Results:

    • InfoGCN++ demonstrates exceptional performance in online action recognition across three benchmarks.
    • Achieves performance equal to or exceeding existing state-of-the-art techniques.
    • Successfully enables real-time action categorization independent of sequence length.

    Conclusions:

    • InfoGCN++ represents a significant advancement over InfoGCN for online skeleton-based action recognition.
    • The model's ability to learn from future movements enhances real-time recognition capabilities.
    • InfoGCN++ has the potential to significantly impact real-time applications in surveillance, robotics, and human-computer interaction.