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    Area of Science:

    • Computer Vision
    • Human Action Recognition

    Background:

    • Human action recognition is a key computer vision research area.
    • Kinect-based techniques are prevalent but lack comprehensive comparative analysis.
    • Feature types (handcrafted, deep learning, depth-based, skeleton-based) significantly impact performance.

    Purpose of the Study:

    • To conduct a thorough comparison of 10 recent Kinect-based human action recognition algorithms.
    • To evaluate algorithms based on feature types and recognition scenarios (cross-subject, cross-view).
    • To analyze algorithm variants and implemented improvements.

    Main Methods:

    • Comparative analysis of 10 Kinect-based algorithms.
    • Evaluation across six benchmark datasets for cross-subject and cross-view recognition.
    • Implementation and enhancement of selected algorithms for inclusion in the study.

    Main Results:

    • Most methods perform better in cross-subject than cross-view action recognition.
    • Skeleton-based features demonstrate superior robustness for cross-view recognition compared to depth-based features.
    • Deep learning features are identified as suitable for large-scale datasets.

    Conclusions:

    • The choice of feature representation is critical for effective human action recognition.
    • Skeleton-based features offer advantages for recognizing actions from different viewpoints.
    • Deep learning approaches show promise for action recognition in extensive datasets.