---
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - video-classification
tags:
  - group-activity-recognition
  - sports-analytics
  - football
  - soccer
  - tracking
  - multimodal
  - graph-neural-networks
  - world-cup-2022
pretty_name: SoccerNet-GAR
dataset_info:
  features:
    - name: event_id
      dtype: string
    - name: match_id
      dtype: string
    - name: action_class
      dtype: string
    - name: timestamp
      dtype: float64
    - name: video_frames
      dtype: image
    - name: tracking_data
      dtype: string
  splits:
    - name: train
      num_examples: 62159
    - name: validation
      num_examples: 12091
    - name: test
      num_examples: 13689
license: other
license_link: https://drive.google.com/file/d/1Efz8yP-baa8CtcMB7SYIID7Bn5sH7sGTB4-u1YdVRf4/view
extra_gated_heading: Request access to SoccerNet GAR dataset
extra_gated_description: >
  Complete the information below to request access. The SoccerNet team will
  review your request and the information you provide. If your request is
  accepted, a personalized Non-Disclosure Agreement will be sent to the email
  address associated with your Hugging Face account.
extra_gated_button_content: Sign the NDA and request access
extra_gated_prompt: >
  By submitting this request, you confirm that the information supplied is
  accurate and that you have read and accept the SoccerNet Non-Disclosure
  Agreement available at
  https://drive.google.com/file/d/1Efz8yP-baa8CtcMB7SYIID7Bn5sH7sGTB4-u1YdVRf4/view.
extra_gated_fields:
  Full Name: text
  Affiliation: text
  Please describe what you plan to do with the dataset: text
  I understand that the use of this dataset is for research and non-commercial use only: checkbox
  I understand that this dataset and the videos are protected by copyright: checkbox
  I agree not to share the dataset with anyone else: checkbox
  I agree to and electronically sign the Non-Disclosure Agreement: checkbox
---

# SoccerNet-GAR: Pixels or Positions? Benchmarking Modalities in Group Activity Recognition

SoccerNet-GAR is a large-scale multimodal dataset for Group Activity Recognition (GAR) built from all 64 matches of the FIFA World Cup 2022 tournament. It provides synchronized broadcast video and player tracking data for 87,939 annotated group activities across 10 action classes, enabling direct comparison between video-based and tracking-based approaches.

## Dataset Details

### Description

SoccerNet-GAR is the first dataset to provide synchronized tracking and video modalities for the same action instances in group activity recognition. For each annotated event, a 4.5-second temporal window is extracted from both the broadcast video and player tracking streams, centered on the event timestamp. In the original sampled representations (`frames` and `tracking`), 16 samples are taken with a 9-frame interval (approximately 3.3 samples per second over a 4.5-second first-to-last-sample span). The additional `videos` and `tracking-full` representations retain the full 144-frame native-rate window; see [Native-FPS MP4 clips](#native-fps-mp4-clips-videos) below.

The dataset contains two input modalities:

- **Video Modality**: Broadcast footage at 720p resolution. Each frame is part of a temporal sequence sampled within the event window, capturing appearance cues, scene context, and visual motion patterns.
- **Tracking Modality**: 2D player positions and 3D ball coordinates sampled at 30 fps, automatically extracted from broadcast footage and manually refined by annotators. Player positions span x in [-60, 60]m, y in [-42, 41]m; ball positions include height z in [-8, 25]m. Each entity state encodes spatial coordinates, entity identity (one-hot encoding), and motion dynamics (displacement vectors between consecutive frames). Positional role metadata (goalkeeper, defender, midfielder, forward) is provided for each player.

| Property | Value |
|---|---|
| **Curated by** | KAUST, University of Liege |
| **Original Data Source** | Gradient Sports (formerly PFF FC) |
| **Total Events** | 87,939 |
| **Matches** | 64 (Football World Cup 2022) |
| **Action Classes** | 10 |
| **Modalities** | Video + Tracking |
| **Avg. Events per Match** | 1,374 |

### Sources

- **Repository:** [https://github.com/drishyakarki/pixels_vs_positions](https://github.com/drishyakarki/pixels_vs_positions)
- **Paper:** [Pixels or Positions? Benchmarking Modalities in Group Activity Recognition (arXiv:2511.12606)](https://arxiv.org/abs/2511.12606)
- **OpenSportsLib:** [https://github.com/OpenSportsLab/opensportslib](https://github.com/OpenSportsLab/opensportslib)

## How to Use

The recommended way to download and use SoccerNet-GAR is through [OpenSportsLib](https://github.com/OpenSportsLab/opensportslib).

The two modalities, video and tracking, are available in four OpenSportsLib-ready representations:

| Branch | Representation | Temporal sampling | OSL input type |
|---|---|---|---|
| [`frames`](https://huggingface.co/datasets/OpenSportsLab/SoccerNet-GAR/tree/frames) | Original sampled RGB frame arrays (`.npy`, `16 × 224 × 224 × 3`, `uint8`) | 16 frames, stride 9 | `frames_npy` |
| [`tracking`](https://huggingface.co/datasets/OpenSportsLab/SoccerNet-GAR/tree/tracking) | Original sampled player/ball tracking (`.parquet`) | 16 sampled frame slots | `tracking_parquet` |
| [`videos`](https://huggingface.co/datasets/OpenSportsLab/SoccerNet-GAR/tree/videos) | Native-resolution MP4 action clips | 144 consecutive source frames at native timing, approximately 29.97 FPS | `video` |
| [`tracking-full`](https://huggingface.co/datasets/OpenSportsLab/SoccerNet-GAR/tree/tracking-full) | Full-rate player/ball tracking (`.parquet`) | 144 native frame slots, approximately 29.97 FPS | `tracking_parquet` |

All four representations retain the same 87,939 action identities, split assignments and labels. Match samples across branches by sample ID within each split. Use `frames` and `tracking` for the original sampled benchmark inputs; use `videos` and `tracking-full` when you need the intervening native-rate frames.

The original data representations remain available through the `frames-raw` and `tracking-raw` branches.

### Installation

```bash
pip install opensportslib
```

### Video (Pixels)

Download the video modality from the canonical `frames` branch:

```python
from opensportslib.tools import download_dataset_split_from_hf

download_dataset_split_from_hf(
    repo_id="OpenSportsLab/SoccerNet-GAR",
    revision="frames",
    split="train",
    output_dir="SoccerNet-GAR",
)
```

Replace `train` with `valid` or `test` to download the other splits.

Train and evaluate:

```python
from opensportslib import model

if __name__ == "__main__":
    myModel = model.classification(
        config="path/to/sngar-frames.yaml",
        data_dir="SoccerNet-GAR",
    )

    myModel.train(
        train_set="SoccerNet-GAR/annotations_train.json",
        valid_set="SoccerNet-GAR/annotations_valid.json",
        use_ddp=False,
    )

    myModel.infer(
        test_set="SoccerNet-GAR/annotations_test.json",
    )
```

### Tracking (Positions)

Download the tracking modality from the canonical `tracking` branch:

```python
from opensportslib.tools import download_dataset_split_from_hf

download_dataset_split_from_hf(
    repo_id="OpenSportsLab/SoccerNet-GAR",
    revision="tracking",
    split="train",
    output_dir="SoccerNet-GAR",
)
```

Replace `train` with `valid` or `test` to download the other splits.

Train and evaluate:

```python
from opensportslib import model

myModel = model.classification(
    config="path/to/classification_tracking.yaml",
    data_dir="SoccerNet-GAR",
)

myModel.train(
    train_set="SoccerNet-GAR/annotations_train.json",
    valid_set="SoccerNet-GAR/annotations_valid.json",
)

myModel.infer(
    test_set="SoccerNet-GAR/annotations_test.json",
)
```

Configuration files for reproducing the experiments in the paper are available in the [pixels_vs_positions](https://github.com/drishyakarki/pixels_vs_positions) repository.

### Native-FPS MP4 clips (`videos`)

The `videos` branch contains one MP4 clip per classification action, regenerated
from the source videos in
[SNGAR-Action-Spotting](https://huggingface.co/datasets/OpenSportsLab/SNGAR-Action-Spotting).
Clips preserve the source resolution, native frame timing and all intervening
frames. They are encoded with H.264/libx264, CRF 18 and `yuv420p`, without audio.
The source timestamps determine timing; clips are not forced to 30 FPS.

Each clip uses the **full sampling window**: from the first of the 16 sampled
frames through one sampling interval after the last, exclusive. With stride 9,
this contains **144 native frames**, approximately **4.805 seconds at 29.97 FPS**.
The approximately 4.5-second span between the first and last sampled frames in
the original representation is therefore shorter than the full native window.

The MP4s preserve action correspondence and validated temporal alignment.
They do not claim pixel-identical reproduction of the original 224 × 224 NumPy
arrays; resizing, historical preprocessing and lossy MP4 encoding can differ.

```python
from opensportslib.tools import download_dataset_split_from_hf

video_files = download_dataset_split_from_hf(
    repo_id="OpenSportsLab/SoccerNet-GAR",
    revision="videos",
    split="test",
    output_dir="SoccerNet-GAR",
    download_format="parquet",
)
```

### Full-FPS tracking (`tracking-full`)

The `tracking-full` branch provides one Parquet clip per classification action,
extracted from the original spotting tracking tables. Each clip contains **144
native frame slots** over the corresponding full action window, including the
frames omitted by the stride-9 sampled representation. Observed source values
and columns are preserved; `frameNum` and `videoTimeMs` retain the source frame
numbers and absolute timestamps.

Some source windows contain missing tracking observations. The release preserves
these slots and adds the boolean **`tracking_valid`** column:

- `true`: an observed source tracking row.
- `false`: a missing source row, represented by a known `frameNum`, null
  `videoTimeMs`, `period = -1` and empty player/ball arrays.

Missing positions are not interpolated. Use the validity flag when constructing
masks or computing motion features; a native frame slot is not necessarily an
observed tracking row. A valid row can still have missing individual entities.
The OSL input type remains `tracking_parquet`; `tracking-full` is the branch name.

```python
from opensportslib.tools import download_dataset_split_from_hf

tracking_files = download_dataset_split_from_hf(
    repo_id="OpenSportsLab/SoccerNet-GAR",
    revision="tracking-full",
    split="test",
    output_dir="SoccerNet-GAR",
    download_format="parquet",
)
```

For both examples, replace `test` with `train` or `valid` to download other splits.
Use the returned download information to locate the reconstructed OSL annotation
manifest. Configure temporal sampling explicitly: `frame_interval: 1` retains
consecutive native frames; stride 9 samples the full-rate inputs back down.
Changing only the branch does not automatically change a model's temporal input.

### Shards and Parquet metadata

Both new branches use OSL's Parquet + WebDataset distribution format. Each split
(`train`: **62,159**, `valid`: **12,091**, `test`: **13,689** samples) has:

```text
<split>/
  metadata.parquet
  shard_manifest.parquet
  shards/
    *.tar
```

The TAR shards contain the MP4 or per-action tracking Parquet files together with
OSL JSON sidecars. The split metadata and shard manifest support sample lookup
and OSL downloads. Sample metadata retains action labels and conversion
provenance, including source hashes/revisions where available, native frame
windows, timing and conversion identity. Original source paths in provenance
are informational, not paths that consumers need to access.

The published benchmark results below refer to the original sampled
representations; they are not new evaluations of the full-rate branches.


## Dataset Structure

### Action Classes

The dataset contains 10 action classes reflecting common football events:

| Class | Count | Proportion |
|---|---|---|
| PASS | 57,521 | 65.4% |
| TACKLE | 10,943 | 12.4% |
| OUT | 5,873 | 6.7% |
| HEADER | 5,723 | 6.5% |
| THROW IN | 2,598 | 3.0% |
| CROSS | 2,175 | 2.5% |
| FREE KICK | 1,788 | 2.0% |
| SHOT | 1,041 | 1.2% |
| GOAL | 30 | 0.2% |
| HIGH PASS | 12 | 0.1% |

The dataset exhibits severe class imbalance (646:1 ratio between PASS and HIGH PASS), reflecting the natural distribution of football events.

### Splits

Data is split at the match level to prevent leakage:

| Split | Matches | Events | Proportion |
|---|---|---|---|
| Train | 45 | 62,159 | 70.7% |
| Validation | 9 | 12,091 | 13.7% |
| Test | 10 | 13,689 | 15.6% |

### Data Quality

- **Player tracking completeness**: 99.9% of 1,485,008 frames contain all 11 players per team.
- **Ball visibility**: 93.4% of frames contain ball tracking data.
- **Event-level ball coverage**: 85.9% of annotated events have complete ball tracking within their temporal window.

## Branches

This repository is organized into the following branches:

| Branch | Contents |
|---|---|
| `main` | Dataset card and documentation. |
| `frames` | Original sampled video frames: 16-frame NumPy arrays per event. |
| `tracking` | Original sampled tracking: 16-frame Parquet clips per event. |
| `videos` | Native-resolution, native-FPS MP4 clips: 144 consecutive frames per event. |
| `tracking-full` | Full-FPS tracking Parquet clips: 144 native frame slots with `tracking_valid` masks. |
| `frames-raw` | Original/raw video representation, with one NPY clip per event. |
| `tracking-raw` | Original/raw tracking representation. |

Use **`frames` and `tracking`** for the original sampled benchmark representations, or **`videos` and `tracking-full`** for native-rate action windows. The `*-raw` branches provide access to the original raw representations.

## Benchmark Results

### Pixels vs. Positions

| Modality | Model | Params | Bal. Acc. | F1 | Training |
|---|---|---|---|---|---|
| **Tracking** | GIN + MaxPool + Positional Edges | **180K** | **77.8%** | **57.0%** | 4 GPU hours |
| Video | VideoMAEv2-B (finetuned) | 86.3M | 60.9% | 50.1% | 28 GPU hours |

The tracking model outperforms the video baseline by 16.9 percentage points in balanced accuracy and 6.9 percentage points in macro F1 while using 479x fewer parameters and training 7x faster.

### Per-Class Comparison (Test Set, Balanced Accuracy)

| Class | Samples | Tracking | Video |
|---|---|---|---|
| PASS | 9,009 | **81.1** | 77.6 |
| TACKLE | 1,690 | **54.0** | 32.2 |
| OUT | 884 | **94.2** | 75.8 |
| HEADER | 867 | 65.2 | **66.3** |
| THROW IN | 12 | **84.2** | 78.6 |
| CROSS | 392 | **86.7** | 77.2 |
| FREE KICK | 347 | **90.4** | 79.4 |
| SHOT | 272 | **76.3** | 63.4 |
| GOAL | 186 | **73.3** | 16.7 |
| HIGH PASS | 30 | **83.3** | 41.7 |

Tracking dominates on 9 of 10 classes, with its largest gains on less frequent classes like GOAL (+56.7 pp) and HIGH PASS (+41.7 pp). Video shows a slight advantage only on HEADER (+1.1 pp). Tracking models learn discriminative features even in severely data-scarce regimes (GOAL: 73.3%, HIGH PASS: 83.3%), whereas video models collapse on these classes (16.7% and 41.7%).

## Uses

### Direct Use

- Benchmarking video-based vs. tracking-based group activity recognition
- Training and evaluating GAR models on football broadcast data
- Studying multimodal fusion approaches combining visual and positional features
- Analyzing spatial interaction patterns in team sports

## Dataset Creation

### Curation Rationale

No standardized benchmark previously existed that aligns broadcast video and tracking data for the same group activities. This made fair, apples-to-apples comparison between video-based and tracking-based approaches impossible. SoccerNet-GAR was created to fill this gap by providing synchronized multimodal observations under a unified evaluation protocol.

### Source Data

The dataset was constructed from the PFF FC website (now Gradient Sports), which provides broadcast videos, player tracking data, and event annotations across all 64 FIFA World Cup 2022 tournament matches.

### Data Cleaning and Alignment

Event annotations are aligned with both input modalities by merging them with tracking streams using UTC timestamps. Three successive filters ensure data quality:

1. **Temporal alignment**: Events where no tracking frame falls within a 10 ms tolerance of the event timestamp are removed.
2. **Modality coverage**: Events lacking corresponding data in either modality are discarded.
3. **Duplicate resolution**: When a single timestamp is annotated with more than one action class (e.g., a goal also labeled as a shot), only the most semantically specific label is retained based on a predefined priority ordering.

Together, these filters remove 6,346 events (6.8% of raw annotations), yielding the final dataset of 87,939 annotated group activities.

### Annotation Process

Event annotations with precise timestamps were created by trained annotators and verified through quality control procedures by PFF FC using both video and tracking views. Each event is labeled with one of 10 group activities and temporally marked at the moment of occurrence.

## Comparison with Existing Datasets

| Dataset | Year | Domain | Events | Classes | Modalities |
|---|---|---|---|---|---|
| CAD | 2009 | Pedestrian | 2,511 | 5 | V |
| Volleyball | 2016 | Volleyball | 4,830 | 8 | V |
| SoccerNet | 2018 | Football | 6,637 | 3 | V |
| NBA | 2020 | Basketball | 9,172 | 9 | V |
| SoccerNet-v2 | 2021 | Football | 110,458 | 17 | V |
| NETS | 2022 | Basketball | 61,053 | 3 | T |
| SoccerNet-BAS | 2024 | Football | 11,041 | 12 | V |
| Cafe | 2024 | Indoor | 10,297 | 6 | V |
| FIFAWC | 2024 | Football | 5,196 | 12 | V |
| **SoccerNet-GAR** | **2026** | **Football** | **87,939** | **10** | **V + T** |

SoccerNet-GAR is the second largest GAR dataset (after SoccerNet-v2) and the only one providing synchronized video and tracking modalities for the same action instances.

## Citation

```bibtex
@article{karki2025pixels,
  title={Pixels or Positions? Benchmarking Modalities in Group Activity Recognition},
  author={Karki, Drishya and Ramazanova, Merey and Cioppa, Anthony and Giancola, Silvio and Ghanem, Bernard},
  journal={arXiv preprint arXiv:2511.12606},
  year={2025}
}
```

## Authors

- **Drishya Karki** (KAUST)
- **Merey Ramazanova** (KAUST)
- **Anthony Cioppa** (University of Liege)
- **Silvio Giancola** (KAUST)
- **Bernard Ghanem** (KAUST)

## Contact

- drishya.karki@kaust.edu.sa / karkidrishya1@gmail.com
- silvio.giancola@kaust.edu.sa

**SoccerNet Challenge 2027 (GAR).** The baseline uses the 16 sampled time steps from the `tracking` branch. Participants are welcome to use any available representation, including the full-rate `videos` and `tracking-full` branches, or to combine video and tracking inputs. The challenge uses the existing `test` split. OSL-format manifests for `train`, `valid`, and `test` across all four representations are available in the [SN-GAR-2027 repository](https://github.com/SoccerNet/sn-gar-2027); see [`RULES.md`](https://github.com/SoccerNet/sn-gar-2027/blob/main/RULES.md) for rules and submission details.