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Dataset Card for MultiSports
Dataset Summary
Spatio-temporal action localization is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. MultiSports is a multi-person dataset of spatio-temporal localized sports actions. Please refer to this paper for more details. Please refer to this repository for evaluation.
Supported Tasks and Leaderboards
Spatial-temporal action localization
Details about evaluation can be found in the GitHub Repository. Previous challenge results can be found in this page and this CodaLab challenge.
Languages
The class labels in the dataset are in English.
Dataset Structure
Data Instances
Demo is available on dataset homepage.
The dataset contains rawframes.tar and multisports_GT.pkl. The GT pkl file is a dictionary with the following structure:
{
'labels': ['label1', 'label2', ...],
'train_videos': [['train_vid_1', 'train_vid_2', ...]],
'test_videos': [['test_vid_1', 'test_vid_2', ...]],
'nframes': {
'vid_1': nframes_1,
'vid_2': nframes_2,
...
},
'resolution': {
'vid_1': resolution_1,
'vid_2': resolution_2,
...
},
'gttubes': {
'vid_1': {
'label_1': [tube_1, tube_2, ...],
'label_2': [tube_1, tube_2, ...],
...
}
...
}
}
Here a tube is a numpy.ndarray with nframes rows and 5 columns <frame number> <x1> <y1> <x2> <y2>.
Data Fields
Raw frames are organized according to their sport category. The pickle file of GT contains the following fields.
labels: list of labels
train_videos: a list with one split element containing the list of training videos
test_videos: a list with one split element containing the list of validation videos
nframes: dictionary that gives the number of frames for each video
resolution: dictionary that output a tuple
(h,w)of the resolution for each videogttubes: dictionary that contains the gt tubes for each video. Gt tubes are dictionary that associates from each index of label, a list of tubes. A
tubeis anumpy.ndarraywithnframesrows and 5 columns<frame number> <x1> <y1> <x2> <y2>.
Please note that the label index starts from 0 and the frame index starts from 1. For the label index i, the label name is labels[i].
Click here to see the full list of MultiSports class labels mapping:
| id | Class |
|---|---|
| 0 | aerobic push up |
| 1 | aerobic explosive push up |
| 2 | aerobic explosive support |
| 3 | aerobic leg circle |
| 4 | aerobic helicopter |
| 5 | aerobic support |
| 6 | aerobic v support |
| 7 | aerobic horizontal support |
| 8 | aerobic straight jump |
| 9 | aerobic illusion |
| 10 | aerobic bent leg(s) jump |
| 11 | aerobic pike jump |
| 12 | aerobic straddle jump |
| 13 | aerobic split jump |
| 14 | aerobic scissors leap |
| 15 | aerobic kick jump |
| 16 | aerobic off axis jump |
| 17 | aerobic butterfly jump |
| 18 | aerobic split |
| 19 | aerobic turn |
| 20 | aerobic balance turn |
| 21 | volleyball serve |
| 22 | volleyball block |
| 23 | volleyball first pass |
| 24 | volleyball defend |
| 25 | volleyball protect |
| 26 | volleyball second pass |
| 27 | volleyball adjust |
| 28 | volleyball save |
| 29 | volleyball second attack |
| 30 | volleyball spike |
| 31 | volleyball dink |
| 32 | volleyball no offensive attack |
| 33 | football shoot |
| 34 | football long pass |
| 35 | football short pass |
| 36 | football through pass |
| 37 | football cross |
| 38 | football dribble |
| 39 | football trap |
| 40 | football throw |
| 41 | football diving |
| 42 | football tackle |
| 43 | football steal |
| 44 | football clearance |
| 45 | football block |
| 46 | football press |
| 47 | football aerial duels |
| 48 | basketball pass |
| 49 | basketball drive |
| 50 | basketball dribble |
| 51 | basketball 3-point shot |
| 52 | basketball 2-point shot |
| 53 | basketball free throw |
| 54 | basketball block |
| 55 | basketball offensive rebound |
| 56 | basketball defensive rebound |
| 57 | basketball pass steal |
| 58 | basketball dribble steal |
| 59 | basketball interfere shot |
| 60 | basketball pick-and-roll defensive |
| 61 | basketball sag |
| 62 | basketball screen |
| 63 | basketball pass-inbound |
| 64 | basketball save |
| 65 | basketball jump ball |
Data Splits
| train | validation | test | |
|---|---|---|---|
| # of tubes | 28514 | 10116 | - |
GT for test split is not provided. Please wait for the new competition to start. Information will be updated in dataset homepage.
Dataset Creation
Curation Rationale
Spatio-temporal action detection is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions.
Source Data
Initial Data Collection and Normalization
After choosing the four sports, we search for their competition videos by querying the name of sports like volleyball and the name of competition levels like Olympics and World Cup on YouTube, and then down- load videos from top search results. For each video, we only select high-resolution, e.g. 720P or 1080P, competition records and then manually cut them into clips of minutes, with less shot changes in each clip and to be more suitable for action detection.
Who are the source language producers?
The annotators of action categories and temporal boundaries are professional athletes of the corresponding sports. Please refer to the paper for more information.
Annotations
Annotation process
(FIRST STAGE) A team of professional athletes generate records of the action la- bel, the starting and ending frame, and the person box in the starting frame, which can ensure the efficiency, accu- racy and consistency of our annotation results.
At least one annotator with domain knowledge double-check the annotations, correct wrong or inaccurate ones and also add missing annotations
(SECOND STAGE) With the help of FCOT tracking algorithm, a team of crowd-sourced annotators adjust bounding boxes of tracking results at each frame for each record.
Double-check each instance by playing it in 5fps and manually correct the inaccurate bounding boxes.
Who are the annotators?
For the first stage, annotators are professional athletes. For the second stage, annotators are common volunteers.
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Authors of this paper
Yixuan Li
Lei Chen
Runyu He
Zhenzhi Wang
Gangshan Wu
Limin Wang
Licensing Information
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Citation Information
If you find this dataset 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}
}
Contributions
Thanks to @Judie1999 for adding this dataset.
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