README

Paper: MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions

Homepage: DeeperAction/MultiSports

Download Data and Annotations

Our data and annotations can now be downloaded from Hugging Face.

You can still register on our competition page, and download data and annotations in the Participate/Data part. But please note that it may take a few moments before we receive the email and accept your request.

MultiSports Dataset License: CC BY_NC 4.0

Download Person Boxes

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.

SlowFast Det.

We run the SlowFast Det. from mmaction2 and change the mmaction/datasets/ava_dataset.py into multisports_dataset.py for our MultiSports Dataset.

Evaluation Tools

evaluate_multisports.py is our evaluation code, which is modified from ACT(https://github.com/vkalogeiton/caffe/blob/act-detector/act-detector-scripts/ACT.py). We change the calculation of PR (Precision-Recall) area same with PASCAL VOC.

Usage:
python3 evaluate_multisports.py task gt_file detection_file -t thr

evaluate frameAP:
python3 evaluate_multisports.py frameAP multisports_GT.pkl frame_detections.pkl
evaluate videoAP:
python3 evaluate_multisports.py videoAP multisports_GT.pkl video_detections.pkl -t 0.2
evaluate videoAP with different thresholds:
python3 evaluate_multisports.py videoAP_all multisports_GT.pkl video_detections.pkl
analyze videoAP error:
python3 evaluate_multisports.py videoAP_error multisports_GT.pkl video_detections.pkl -t 0.2

frame_detections.pkl is a list. Every item is a numpy array with shape (8,) , numpy.array([video_index, frame_number, label_index, score, x1, y1, x2, y2]). video_index is the index of the video in the multisports_GT['test_videos'][0], which starts from 0. For example, 0 is the index of 'aerobic_gymnastics/v_crsi07chcV8_c004' in multisports_GT['test_videos'][0]. frame_number starts from 1. label_index starts from 0. score is the score of this box, which affects the frame mAP result.

video_detections.pkl is a dictionary that associates from each index of label (start from 0), a list of tubes. A tube is a tuple (tube_v, tube_score, tube_boxes). tube_v is the video name, such as 'aerobic_gymnastics/v_crsi07chcV8_c004'. tube_score is the score of this tube, which affects the video mAP result. tube_boxes is a numpy array with tube-length rows and 6 columns, every row is [frame number, x1, y1, x2, y2, box_score]. frame number starts from 1. box_score is the single frame's confidence and does not affect the video mAP result.

We provide examples of frame_detections.pkl and video_detections.pkl of slowonly in examples, whose evaluation results with different PR area calculation are shown below.

If you find our code or paper useful, please cite as

@InProceedings{Li_2021_ICCV,
    author    = {Li, Yixuan and Chen, Lei and He, Runyu and Wang, Zhenzhi and Wu, Gangshan and Wang, Limin},
    title     = {MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {13536-13545}
}