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Available Model List

- Refer to each Branch of this repository

@article{2s-AGCN,
  title={Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition},
  author={Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu},
  journal = {IEEE Conference on Computer Vision and Pattern Recognition, 2019},
  year={2019}
}
@article{PoseC3D,
  title={Revisiting Skeleton-based Action Recognition},
  author={Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, Bo Dai},
  journal = {IEEE Conference on Computer Vision and Pattern Recognition, 2022},
  year={2022}
}
@article{CRT-GCN,
  title={Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition},
  author={Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, Weiming Hu},
  journal = {International Conference on Computer Vision, 2021},
  year={2021}
}

Skeleton-Based Action Recognition Methods

- Skeleton-based action recognition

  • A computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data
  • The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data
  • The gcn-based method (extracting visual features) and the cnn-based method (extracting graph features) are mainly used.

- Goals of this repository

  • Various skeleton-based action recognition methods implementation/test/customization etc.

Datasets

- NTU RGB+D

  • Download link: https://github.com/shahroudy/NTURGB-D

  • Data volume and classes

    • 56,000 action clips in 60 action classes
    • Kind of classes
      • NTU RGB+D: A1 to A60, NTU RGB+D 120: A1 to A120

  • Cross-subject (X-Sub) Train/Valid subset

    • Training set: 40,320 clips
    • validation set: 16,560
    • The actors in the two subsets are different
  • Cross-view (X-View) Train/Valid subset

    • Training set (captured by cameras 2 and 3): 37,920 clips
    • validation set (captured by camera 1): 18,960 clips
  • Details of data

    • Each action is captured by 3 cameras at the same height but from different horizontal angles: −45, 0, 45
    • 3D joint locations of each frame detected by Kinect depth sensors
    • 25 joints for each subject in the skeleton sequences, while each video has no more than 2 subjects

- Kinetics-skeleton

- FineGym

  • Details of data

    • Organizing both the semantic and temporal annotations hierarchically

    • Semantic annotations: three-level categories of actions and sub-actions

      • Events
        • Coarsest level of the hierarchy
        • Actions belonging to different gymnastic routines
        • E.g. vault (VT), floor exercise (FX), uneven-bars (UB), and balance-beam (BB)
      • Sets
        • Mid-level categories
        • Describing sub-actions
        • A set holds several technically and visually similar elements
        • E.g. Flight-handspring, Beam-Turns, Flight-Salto etc.
      • Elements
        • Finest granularity are element categories
        • Equips sub-actions with more detailed descriptions than the set categories
        • E.g. a sub-action instance of the set beam-dismounts could be more precisely described as double salto backward tucked or other element categories in the set.
    • Two-levels temporal annotations

      • Locations of all events in a video
      • Locations of sub-actions in an action instance (i.e. event instance)

- Goyang Taekwondo Dataset

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