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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- pose-based gait recognition |
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- identity recognition based on basketball actions |
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size_categories: |
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- 10M<n<100M |
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--- |
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Our paper: |
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### 《STGGait: A Graph Transformer Network for Pose-based Gait Recognition》, |
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has been accepted by: [IEEE International Conference on Multimedia & Expo 2025 (ICME 2025)](https://2025.ieeeicme.org/)! |
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### We will make the dataset publicly available after the conference. |
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## **Dataset Card for MultiSubjects-Gait** |
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MutiSubjects-Gait is built based on [MutiSubjects](https://www.sciencedirect.com/science/article/abs/pii/S1077314224002741). |
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MutiSubjects-Gait is the first dataset available for identity recognition based on basketball actions, exploring the application of gait recognition in complex sports scenarios. |
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We divide it into three subsets according to different actions: MutiSubjects-D, MutiSubjects-P, and MutiSubjects-S. |
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We use [HRNet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch) to extract 2D human keypoint data and align the structure of the dataset with [CASIA-B Pose](https://www.scidb.cn/en/detail?dataSetId=8ec62efd66a544939e821edeccc1f35c) to facilitate experimental comparison. |
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### Dataset Information |
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MutiSubjects-D: |
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```json |
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{ |
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Action: Dribbling |
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Number of subjects: 424 |
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Training ID: 001-324 |
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Test ID：325-424 |
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Gallery: #01 |
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Probe: #02-03 |
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} |
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``` |
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MutiSubjects-P: |
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```json |
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{ |
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Action: Layup |
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Number of subjects: 209 |
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Training ID: 001-159 |
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Test ID：160-209 |
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Gallery: #01 |
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Probe: #02 |
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} |
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``` |
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MutiSubjects-S: |
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```json |
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{ |
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Action: Shooting |
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Number of subjects: 659 |
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Training ID: 001-527 |
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Test ID：528-659 |
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Gallery: #01 |
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Probe: #02-03 |
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} |
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``` |
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### Dataset Curators |
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Authors of STGGait |
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* Wansong Qin |
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* Zhijie Han |
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* Yaru Li |
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### Citation Information |
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...... |
