We will release our baseline codes and models soon.
Paper: MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions
Homepage: DeeperAction/MultiSports
Please register on our competition page, and download data and annotations in the Participate/Data part.
MultiSports Dataset License: CC BY_NC 4.0
We provide the person boxes generated by the person detector of Faster R-CNN with a ResNeXt-101-FPN backbone in the links below. We finetune the person detector only with the train set data.
Onedrive: https://1drv.ms/f/s!AtjeLq7YnYGRe3eQMuQk5GYYu40
Baidu Wangpan: https://pan.baidu.com/s/1zOylA-idz2foeEaU1gx6sw (password: 5ccx)
The pkl file has the below data structure:
{"video_name,frame_number":boxes}
video_name: str
frame_number: int (starts from 1)
boxes: a numpy array with n rows and 5 columns, <x1> <y1> <x2> <y2> <score>. x1, x2, y1, y2 are normalized with respect to frame size, which are between 0.0-1.0. We only save the box with score higher than 0.05.
evaluate.py is our evaluation code, which is modified from ACT(https://github.com/vkalogeiton/caffe/blob/act-detector/act-detector-scripts/ACT.py). You can register on this website and submit predictions for test set evaluation in the Participate/Submit part.
VideomAP_error.py is our video mAP error analysis code, which is based on the frame mAP error analysis code of ACT(https://github.com/vkalogeiton/caffe/blob/act-detector/act-detector-scripts/ACT.py).
submissions.zip is our example submission file on 50% test set.
If you find our code or paper useful, please cite as
@article{li2021multisports,
title={MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions},
author={Li, Yixuan and Chen, Lei and He, Runyu and Wang, Zhenzhi and Wu, Gangshan and Wang, Limin},
journal={arXiv preprint arXiv:2105.07404},
year={2021}
}