metadata
task_categories:
- video-classification
tags:
- micro-action
gated: true
gated_fields:
- name: Full Name
description: Your full name
- name: Affiliation
description: Your institution, organization, or university
- name: Email
description: A valid academic or professional email address
- name: Intended Use
description: Briefly describe how you intend to use this dataset
The dataset contains 2D pose annotations for the Micro-Action 52 dataset.
The data format follows the skeleton annotation structure defined by MMAction2
The 2D keypoints were extracted using OpenPose, including a total of 28 keypints, consisting of:
- 18 body keypoints
- 5 left-hand fingertip keypoints
- 5 right-hand fingertip keypoints
The connections between keypoints (i.e., the skeleton graph) are defined as follows:
skeletons = ((4, 3), (3, 2), (7, 6), (6, 5), (13, 12), (12, 11), (10, 9),
(9, 8), (11, 5), (8, 2), (5, 1), (2, 1), (0, 1), (15, 0),
(14, 0), (17, 15), (16, 14),
(23, 4), (24, 4), (25, 4), (26, 4), (27, 4),
(18, 7), (19, 7), (20, 7), (21, 7), (22, 7))
If you find this dataset useful in your research, please consider citing the following works:
@inproceedings{li2025prototypical,
title={Prototypical calibrating ambiguous samples for micro-action recognition},
author={Li, Kun and Guo, Dan and Chen, Guoliang and Fan, Chunxiao and Xu, Jingyuan and Wu, Zhiliang and Fan, Hehe and Wang, Meng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={39},
number={5},
pages={4815--4823},
year={2025}
}
@article{guo2024benchmarking,
title={Benchmarking Micro-action Recognition: Dataset, Methods, and Applications},
author={Guo, Dan and Li, Kun and Hu, Bin and Zhang, Yan and Wang, Meng},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2024},
volume={34},
number={7},
pages={6238-6252}
}
If you have any questions or feedback, please contact us at [kunli.hfut@gmail.com].