Datasets:
language:
- en
license: agpl-3.0
tags:
- video
- soccernet
- football
- soccer
- sport
- action-anticipation
size_categories:
- 10K<n<100K
source_datasets:
- SoccerNet/SN-BAS-2024
SoccerNet Challenge 2026 - Action Anticipation Dataset
The SoccerNet Action Anticipation dataset splits the 2024 ball action spotting dataset into 30 second clips, which can then be used to anticipate between 10 action classes that will happen 5 seconds into the future. This is the dataset used for 2026 SoccerNet Action Anticipation challenge.
Relevant Links
- Repository: https://github.com/MohamadDalal/FAANTRA
- Paper: https://huggingface.co/papers/2504.12021
Uses
Direct Use
The direct use of this dataset is to participate in the 2026 SoccerNet Action Anticipation challenge. For a quick demo the FAANTRA repository can be used to train and evaluate on the dataset.
Other Use
The dataset can be used for further action anticipation research within the soccer field.
Download
The setup_dataset_BAA.py script inside the FAANTRA repository can be used to download and setup the dataset.
Dataset Structure
The dataset is structured as:
224p
|_train.zip
|_valid.zip
|_test.zip
|_challenge.zip
720p
|_train.zip
|_valid.zip
|_test.zip
|_challenge.zip
Each zip file contains a split at a specific resolution. Each split is structured as:
split
|_clip_1
|_{224p|720p}.mp4
|_clip_2
|_{224p|720p}.mp4
...
|_Labels-ball.json
The challenge split however, does not contain annotations, and therefore does not have the Labels-ball.json file.
Citation
BibTeX:
@InProceedings{Dalal_2025_CVPR,
author = {Dalal, Mohamad and Xarles, Artur and Cioppa, Anthony and Giancola, Silvio and Van Droogenbroeck, Marc and Ghanem, Bernard and Clap\'es, Albert and Escalera, Sergio and Moeslund, Thomas B.},
title = {Action Anticipation from SoccerNet Football Video Broadcasts},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2025},
pages = {6126-6137}
}