Datasetpublic

imva-upf/SoccerHigh

⚽ 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 HaroACM 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… See the full description on the dataset page: https://huggingface.co/datasets/imva-upf/SoccerHigh.

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Dataset Card

⚽ SoccerHigh

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

SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization ![arXiv](https://arxiv.org/abs/2509.01439) ![ACM DL](https://dl.acm.org/doi/pdf/10.1145/3728423.3759410) 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/SoccerNetrawHQ

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

📂 Dataset Structure

The dataset is organized hierarchically:

text

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

bibtex

@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.