Henu-Software/Henu-MultiSubjects

Dataset Card for MultiSubjects

MultiSubjects Introduction

MultiSubjects is a multi-subject single-player basketball action dataset for amateur basketball action recognition.

Please refer to this paper for more details.

If you wish to access it, please contact us and specify your organization and purpose.

Our email: qws_nohalo@163.com; hanzhijie@126.com.

Languages

The class labels in the dataset are in English.

Action labels

d(0): dribble

p(1): lay up

s(2): shoot

Dataset Structure

The file: Subjects1000.

{
  {-1(subjects ID lable)
   -d(action label)
    -1_d_1.mp4('id'_'action label'_'video number')
   -p
    ...
   -s
    ...}

    .
    .
    .

  {-1000(subjects ID lable)
   -d
    -1000_d_1.mp4
     ...
   -p
    ...
   -s
    ...}
}

The file: MultiSubjects_train_val_test, for video action recognition.

{
  train
   -'id'_'actionlabel video'_'number'.mp4
    ...
  val
   ...
  test
   ...

  train.txt
   -'video_name.mp4' 'action_label'
  val.txt
   ...
  test.txt
   ...
}

The file: MultiSubjects_SA, for skeleton-based action recognition.

We use mmaction2 to extract 2D human keypoints and construct the dataset in the format of the NTU dataset, divided into training and validation sets.

{
  train.pkl
  val.pkl
}

The file: key_points_csv, the coordinates of 33 keypoints detected frame-wise by BlazePose. The structure of 'id''action label''video number'.csv

{
  'Frame number'{1,2,3...}
  'Keypoint_0'{x, y, z}, 'keypoint_1'{x, y, z} ... 'keypoint_32'{x, y, z}
}

The file: MultiSubjects_person_GT.

{
  'Video name'{'id'_'action label'_'video number'.mp4 ...}
  'Frame number'{1,2,3...}
  'Box'{<x1, y1, x2, y2> ...}
  'Action label'{0, 1, 2}
  'Joint'{0}
}

Dataset Curators

Authors of this paper

• Zhijie Han
• Wansong Qin
• Yalu Wang
• Qixiang Wang
• Yongbin Shi

Citation Information

@article{han2024multisubjects,
  title={MultiSubjects: A multi-subject video dataset for single-person basketball action recognition from basketball gym},
  author={Han, Zhijie and Qin, Wansong and Wang, Yalu and Wang, Qixiang and Shi, Yongbin},
  journal={Computer Vision and Image Understanding},
  pages={104193},
  year={2024},
  publisher={Elsevier}
}