https://github.com/anibali/aspset-510
A large-scale video dataset for the training and evaluation of 3D human pose estimation models
https://github.com/anibali/aspset-510
dataset human-pose-estimation
Last synced: 11 months ago
JSON representation
A large-scale video dataset for the training and evaluation of 3D human pose estimation models
- Host: GitHub
- URL: https://github.com/anibali/aspset-510
- Owner: anibali
- License: cc0-1.0
- Created: 2020-05-12T02:29:16.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-02-04T00:10:56.000Z (over 2 years ago)
- Last Synced: 2025-03-18T00:01:47.096Z (about 1 year ago)
- Topics: dataset, human-pose-estimation
- Language: Python
- Homepage:
- Size: 215 KB
- Stars: 44
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
**UPDATE 2024-02-04: Test set annotations have now been released. [Click to download](https://archive.org/download/aspset510/aspset510_v1_test-joints_3d.tar.gz).**
# ASPset-510
ASPset-510 (**A**ustralian **S**ports **P**ose Data**set**) is a large-scale video dataset for
the training and evaluation of 3D human pose estimation models. It contains 17 different amateur
subjects performing 30 sports-related actions each, for a total of 510 action clips.
This repository contains Python code for working with ASPset-510.
If you don't want to use these scripts and would prefer to directly download the data yourself,
ASPset-510 is available on the Internet Archive at
[https://archive.org/details/aspset510](https://archive.org/details/aspset510).
## Requirements
### Core
```bash
$ conda env create -f environment.yml
```
* python >= 3.6
* numpy
* [ezc3d](https://github.com/pyomeca/ezc3d)
* [posekit](https://github.com/anibali/posekit)
### GUI (Optional)
```bash
$ conda env update -f environment-gui.yml
```
* [PyOpenGL](http://pyopengl.sourceforge.net/)
* [glfw](https://github.com/FlorianRhiem/pyGLFW)
* matplotlib
### PyTorch (Optional)
```bash
$ conda env update -f environment-torch.yml
```
## Scripts
### Downloading the dataset
`download_data.py` downloads and extracts ASPset-510 data.
Example usage:
```bash
$ python src/aspset510/bin/download_data.py --data-dir=./data
```
Note that by default the original archive files will be downloaded and kept in the `archives`
subdirectory of whichever path you set using `--data-dir`. To set a different path for the
archives, use the `--archive-dir` option. To download the archives without extracting them,
use the `--skip-extraction` option.
### Browsing clips from the dataset
`browse_clips.py` provides a graphical user interface for browsing clips from ASPset-510.
Example usage:
```bash
$ python src/aspset510/bin/browse_clips.py --data-dir=./data
```
## Acknowledgments and license
ASPset-510 is brought to you by [La Trobe University](https://www.latrobe.edu.au/) and the
[Australian Institute of Sport](https://www.ais.gov.au/). It is dedicated to the public
domain under the [CC0 1.0 license](https://creativecommons.org/publicdomain/zero/1.0/).
If you find this dataset useful for your own work, please cite the following paper:
```
@article{nibali2021aspset,
title={{ASPset}: An Outdoor Sports Pose Video Dataset With {3D} Keypoint Annotations},
author={Nibali, Aiden and Millward, Joshua and He, Zhen and Morgan, Stuart},
journal={Image and Vision Computing},
pages={104196},
year={2021},
issn={0262-8856},
doi={https://doi.org/10.1016/j.imavis.2021.104196},
url={https://www.sciencedirect.com/science/article/pii/S0262885621001013},
publisher={Elsevier}
}
```