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Things you need to know about PYSKL data format

PYSKL provides pre-processed pickle annotations files for training and testing. Below we demonstrate the format of the annotation files and provide the download links.

The format of the pickle files

Each pickle file corresponds to an action recognition dataset. The content of a pickle file is a dictionary with two fields: split and annotations

  1. Split: The value of the split field is a dictionary: the keys are the split names, while the values are lists of video identifiers that belong to the specific clip.
  2. Annotations: The value of the annotations field is a list of skeleton annotations, each skeleton annotation is a dictionary, containing the following fields:
    1. frame_dir (str): The identifier of the corresponding video.
    2. total_frames (int): The number of frames in this video.
    3. img_shape (tuple[int]): The shape of a video frame, a tuple with two elements, in the format of (height, width). Only required for 2D skeletons.
    4. original_shape (tuple[int]): Same as img_shape.
    5. label (int): The action label.
    6. keypoint (np.ndarray, with shape [M x T x V x C]): The keypoint annotation. M: number of persons; T: number of frames (same as total_frames); V: number of keypoints (25 for NTURGB+D 3D skeleton, 17 for CoCo, 18 for OpenPose, etc. ); C: number of dimensions for keypoint coordinates (C=2 for 2D keypoint, C=3 for 3D keypoint).
    7. keypoint_score (np.ndarray, with shape [M x T x V]): The confidence score of keypoints. Only required for 2D skeletons.

Note:

  1. For Kinetics400, things are a little different (for storage saving and training acceleration):
    1. The fields keypoint, keypoint_score are not in the annotation file, but stored in many different kpfiles.
    2. A new field named raw_file, which specifies the file path of the kpfile that contains the skeleton annotation of this video.
    3. Each kpfile is a dictionary: key is the frame_dir, value is a dictionary with a single key keypoint. The value of keypoint is an ndarray with shape [N x V x C]. N: number of skeletons in the video; V: number of keypoints; C (C=3): number of dimensions for keypoint (x, y, score).
    4. A new field named frame_inds, indicates the corresponding frame index of each skeleton.
    5. A new field named box_score, indicates the corresponding bbox score of each skeleton.
    6. A new field named valid, indicates how many frames (with valid skeletons) left when we only keep skeletons with bbox scores larger than a threshold.
    7. We cache the kpfiles in memory with memcache and query with frame_dir to obtain the skeleton annotation. Kinetics-400 skeletons are converted to normal skeleton format with operator DecompressPose.

You can download an annotation file and browse it to get familiar with our annotation formats.

Download the pre-processed skeletons

We provide links to the pre-processed skeleton annotations, you can directly download them and use them for training & testing.

For Kinetics400, since the skeleton annotations are large, we do not provide the direct download links on aliyun. Please use the following link to download the kpfiles and extract it under $PYSKL/data/k400 for Kinetics-400 training & testing: https://mycuhk-my.sharepoint.com/:u:/g/personal/1155136485_link_cuhk_edu_hk/EeyDCVskqLtClMVVwqD53acBF2FEwkctp3vtRbkLfnKSTw?e=B3SZlM

Process NTURGB+D raw skeleton files

  1. Assume that you are using the current directory as the working directory.
  2. Download the raw skeleton files from the official repo of NTURGB+D, unzip and place all .skeleton files in a single folder (named nturgb+d_skeletons in my example).
  3. Run python ntu_preproc.py to generate processed skeleton annotations, it will generate ntu60_3danno.pkl and ntu120_3danno.pkl (If you also downloaded the NTURGB+D 120 skeletons) under your current working directory.

PS: For the best pre-processing speed, change num_process in ntu_preproc.py to the number of cores that your CPU has.

BibTex items for each provided dataset

% NTURGB+D
@inproceedings{shahroudy2016ntu,
  title={Ntu rgb+ d: A large scale dataset for 3d human activity analysis},
  author={Shahroudy, Amir and Liu, Jun and Ng, Tian-Tsong and Wang, Gang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1010--1019},
  year={2016}
}
% NTURGB+D 120
@article{liu2019ntu,
  title={Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding},
  author={Liu, Jun and Shahroudy, Amir and Perez, Mauricio and Wang, Gang and Duan, Ling-Yu and Kot, Alex C},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={42},
  number={10},
  pages={2684--2701},
  year={2019},
  publisher={IEEE}
}
% NW-UCLA
@inproceedings{wang2014cross,
title={Cross-view action modeling, learning and recognition},
author={Wang, Jiang and Nie, Xiaohan and Xia, Yin and Wu, Ying and Zhu, Song-Chun},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={2649--2656},
year={2014}
}
% Kinetics-400
@inproceedings{carreira2017quo,
  title={Quo vadis, action recognition? a new model and the kinetics dataset},
  author={Carreira, Joao and Zisserman, Andrew},
  booktitle={proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6299--6308},
  year={2017}
}
% PYSKL
@inproceedings{duan2022pyskl,
  title={Pyskl: Towards good practices for skeleton action recognition},
  author={Duan, Haodong and Wang, Jiaqi and Chen, Kai and Lin, Dahua},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  pages={7351--7354},
  year={2022}
}