Datasets:

imva-upf
/

SoccerHigh

pretty_name: SoccerHigh
license: cc-by-nc-sa-4.0
task_categories:
  - video-classification
  - feature-extraction
tags:
  - soccer
  - sports
  - video
  - video-summarization
  - highlight-detection
  - temporal-annotations
homepage: https://ipcv.github.io/SoccerHigh/
repository: https://github.com/IPCV/SoccerHigh
dataset_size: 22GB

⚽ SoccerHigh

This dataset provides annotations and pre-extracted features for the SoccerHigh benchmark introduced in:

SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization

Artur Díaz-Juan, Coloma Ballester, Gloria Haro
ACM MMSports 2025

📦 Contents

Highlight summary annotations
Train / validation / test splits
Pre-extracted visual features (no raw videos)

All data are provided as .npy feature arrays, .srt temporal annotations, and .json metadata files.

🌍 Data Source

Originally hosted at: https://github.com/IPCV/SoccerHigh

⚠️ Important Note

Raw videos are NOT included.
Videos must be obtained separately from the SoccerNet dataset: https://huggingface.co/datasets/SoccerNet/SoccerNet_raw_HQ

The provided features are non-invertible and intended solely for research purposes.

📂 Dataset Structure

The dataset is organized hierarchically:

train.txt
validation.txt
test.txt
<league>/
├── <season>/
│   ├── <game>/
│   │   ├── 1_HQ_224p_VideoMAEv2_Giant_K710_1408.npy
│   │   ├── 1_HQ_224p_VideoMAEv2_SmallFromGiant_K710_384.npy
│   │   ├── 1_intervals.srt
│   │   ├── 2_HQ_224p_VideoMAEv2_Giant_K710_1408.npy
│   │   ├── 2_HQ_224p_VideoMAEv2_SmallFromGiant_K710_384.npy
│   │   ├── 2_intervals.srt
│   │   ├── Labels-summary.json

📝 Files per game

1_HQ_224p_VideoMAEv2_Giant_K710_1408.npy
Frame features from the game's first half, extracted with the VideoMAEv2-Giant backbone.
1_HQ_224p_VideoMAEv2_SmallFromGiant_K710_384.npy
Frame features from the game's first half, extracted with the VideoMAEv2-SmallFromGiant backbone.
1_intervals.srt
Annotated temporal segments for the first half in .srt format.
2_HQ_224p_VideoMAEv2_Giant_K710_1408.npy
Frame features from the game's second half, extracted with the VideoMAEv2-Giant backbone.
2_HQ_224p_VideoMAEv2_SmallFromGiant_K710_384.npy
Frame features from the game's second half, extracted with the VideoMAEv2-SmallFromGiant backbone.
2_intervals.srt
Annotated temporal segments for the second half in .srt format.
Labels-summary.json
Metadata describing the game (teams, date, score, video URLs, and annotations).

🚀 Usage

This dataset is distributed as structured files (features and annotations). Users can download the data directly from Hugging Face and load it using custom data pipelines.

A Hugging Face datasets loading script is not provided at this time.

📖 Citation

@inproceedings{10.1145/3728423.3759410,
  author = {D\'{\i}az-Juan, Artur and Ballester, Coloma and Haro, Gloria},
  title = {SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization},
  year = {2025},
  isbn = {9798400711985},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3728423.3759410},
  doi = {10.1145/3728423.3759410},
  booktitle = {Proceedings of the 8th International ACM Workshop on Multimedia Content Analysis in Sports},
  pages = {121–130},
  numpages = {10},
  location = {Dublin, Ireland},
  series = {MMSports '25}
}

⚖️ Legal Notice

This dataset contains only annotations and non-invertible feature representations derived from videos available in the SoccerNet dataset.

Redistribution of raw videos is not permitted. Any access to or use of the original videos must comply with the SoccerNet license and terms of use.