FOOTPASS

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Baselines for the SoccerNet Player-Centric Ball-Action Spotting Challenge 2026

FOOTPASS 최신버젼 다운로드

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# FOOTPASS: Footovision Play-by-Play Action Spotting in Soccer Dataset  
### _Ressources for training the baselines of the SoccerNet Player-Centric Ball-Action Spotting Challenge 2026_

---

![Graphical Abstract](https://raw.githubusercontent.com/JeremieOchin/FOOTPASS/main/graphical_abstract.png)

---

## 🏟️ Overview

**FOOTPASS** (Footovision Play-by-Play Action Spotting in Soccer) introduces the **first player-centric, multi-modal, multi-agent dataset** designed for *play-by-play* action spotting in full-length soccer broadcast videos.

This benchmark supports the **SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge**, aiming to identify **who performs what and when** in real broadcast footage.

---

## 🎯 Motivation

Why FOOTPASS?
Most public soccer datasets either (i) annotate sparse/global events with timestamps only—without telling you who performed the action—or (ii) provide tactical/per-player logs but without the original broadcast video. FOOTPASS closes this gap by aligning broadcast video with player-centric, ball-related actions and team/jersey/role information, plus tracking and spatiotemporal data. The result is a benchmark purposely designed to foster joint research in visual reasoning and tactical understanding under realistic match conditions.

It integrates:
- Full HD broadcast **videos** (25 fps)
- **Spatiotemporal data** (positions, velocities)
- **Tracking data** (tracklets: sequences of bounding boxes)
- **Team, Jersey numbers and Role information**
- **Play-by-play events** with frame-level anchors: sequence of (frame, team, jersey, class)

---

## 📦 Dataset Structure

FOOTPASS contains **54 complete matches** from top European competitions (2023–24):  
Ligue 1, Bundesliga, Serie A, La Liga, and UEFA Champions League — representing 50 teams.

| Split | Matches | Events |
|:------|:--------:|-------:|
| Train | 48 | 91 327 |
| Validation | 3 | 6 070 |
| Challenge/Test | 3 | 5 595 |

Each event is represented as: (frame, team, jersey, class)

where  
- `frame` = frame index (0-based)  
- `team` = 0 (left) / 1 (right)  
- `jersey` = player shirt number  
- `class` = action category

---

## 📊 Dataset Statistics

- **102 992 annotated events**
- **81.5 %** have visible bounding boxes  
- **Class imbalance** mirrors real play: Pass (49.9 %), Drive (39.0 %), others ≈ 11 %
- **13 player roles** defined (Goalkeeper → Right Back)

---

## ⚽ Action Classes

| Class | Description |
|:------|:-------------|
| **Drive** | Player carries the ball after reception |
| **Pass** | Ball strike toward a teammate |
| **Cross** | Pass from wide area toward penalty area |
| **Shot** | Attempt on goal |
| **Header** | Intentional ball contact with head |
| **Throw-in** | Restart from sideline |
| **Tackle** | Legal dispossession |
| **Block** | Interception of opponent’s shot or pass |

These instantaneous events (one frame per action) form the *play-by-play* record of a match.

---

## 🧩 Player Roles

Each player in FOOTPASS is associated with one of 13 predefined **tactical roles**, determined from formations, trajectories, and expert annotation.  
These roles help integrate **multi-agent reasoning** and **tactical structure** into action spotting models.

| Role ID | Tactical Role |
|:--------:|:--------------|
| **1** | Goalkeeper |
| **2** | Left Back |
| **3** | Left Central Back |
| **4** | Mid Central Back |
| **5** | Right Central Back |
| **6** | Left Midfielder |
| **7** | Right Midfielder |
| **8** | Defensive Midfielder |
| **9** | Attacking Midfielder |
| **10** | Left Winger |
| **11** | Right Winger |
| **12** | Central Forward |
| **13** | Right Back |

---

## 🧠 Baseline Methods

This repository provides reference implementations:

| Baseline | Description |
|:----------|:-------------|
| **TAAD** | Track-Aware Action Detector (visual STAD baseline) |
| **TAAD + GNN** | Adds spatio-temporal graph reasoning (multi-agent context) |
| **TAAD + DST** | Denoising Sequence Transduction model with game-level reasoning |

All models output predictions of the form `(frame, team, jersey, class)`.

---

## 📈 Evaluation Protocol

Evaluation follows the official **SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge**:

### 📁 Submission Format

Predictions must be submitted as **JSON files**, one per split (`train`, `val`, `challenge`).  
Each JSON file should follow the same structure as those in the repository under `playbyplay_PRED/`.

Example (simplified excerpt):
```json
{
  "keys": [
    "game_18_H1",
    "game_18_H2",
    ....,
    ....,
  ],
  "events": {
    "game_18_H1": [
      [
        39,
        0,
        81,
        2,
        0.8043681740760803
      ],
      [
        74,
        0,
        3,
        1,
        0.7428927898406982
      ],
      ....
      [
        142815,
        0,
        3,
        1,
        0.962439775466919
      ]
    ]
  }
}
```
👉 See the baseline example playbyplay_TAAD_val.json

---

📏 Metrics

Primary: F1 score @ confidence threshold τ = 15 %

Temporal tolerance: ± 12 frames

Evaluation: via SoccerNet evaluation server
(ground-truth for the challenge set remains hidden)

---

## 🔗 Access

| Resource | Location |
|:----------|:----------|
| **Dataset** | [Hugging Face – SoccerNet](https://huggingface.co/datasets/SoccerNet/SN-PCBAS-2026/tree/main)
| **Videos** | Available via **SoccerNet NDA** [NDA Form](https://docs.google.com/forms/d/e/1FAIpQLSfYFqjZNm4IgwGnyJXDPk2Ko_lZcbVtYX73w5lf6din5nxfmA/viewform)|
| **Evaluation** | [Codabench challenge](https://www.codabench.org/competitions/11232/) |
| **Paper** | [`FOOTPASS_preprint_September_2025.pdf`](https://hal.science/hal-05373478v1) |

The evaluation server will be prolonged after the SoccerNet challenge in order to allow researchers to evaluate their methods on the test set.
---

## 🚀 Getting Started

Environment for TAAD and TAAD + DST

- Python 3.11.5
- PyTorch 2.1.0
- Torchvision 0.16.0
- Albumentations 2.0.8
- Numpy 1.26
- Opencv-python 4.11
- Decord 0.6
- h5py 3.14

Environment for TAAD + GNN

- Python 3.11.5
- PyTorch 2.1.0
- Torchvision 0.16.0
- torch_geometric 2.6.1
- Albumentations 2.0.8
- Numpy 1.26
- Opencv-python 4.11
- Decord 0.6
- h5py 3.14

Clone the repository :
git clone https://github.com/Footovision/FOOTPASS.git
cd FOOTPASS

Download the dataset :
Download the videos and annotations files from the links above (see section "Access"). You will need to fill-in and submit the NDA form from the SoccerNet website to get the password that allows you to extract the files.

Put the videos into the \videos folder of the cloned repo, and the annotations in \data (there are .txt files that shows where to store these files in the relevant folders).

Once the dataset is placed in the proper folders and the environments are properly configured, you can start training the models :
- TAAD : just run the train_TAAD_Baseline.py file : python train_TAAD_Baseline.py - Check the code to see the options.
- TAAD+GNN : python train_GNN.py - Check the code to see the options.
- TAAD+DST : the Denoising Sequence Transduction model learns how to post-process long sequences of "noisy" predictions from the TAAD model using "tactical priors" (see the paper to learn more about it ==> section "Citation"). Therefore, before training it, you need to first train the TAAD baseline model and then make predictions with it over the whole training set. The line : python run_TAAD_on_matches.py does just that (check the file for the options : on which set to run it, where to store the predictions etc.). Then use NPpreds2HDF5.py to convert these stored raw predictions to HDF5 files used in the \utils\DST_dataset.py file (the Dataset class used by the Dataloader in the train_DST.py file). Then run python train_DST.py - Check the file for the options

Format conversion :
- Use NPpreds2JSON.py to convert stored raw predictions (Numpy array) as a JSON file, and performing a NMS. Check the code to see the options.
- Use NPpreds2HDF5.py to convert stored raw predictions (Numpy array) from the TAAD baseline model to HDF5 file, for training the DST model. Check the file for options.

Evaluation :
- TAAD : you can use python run_TAAD_on_matches.py to run the trained model on the validation or test set, and then use NPpreds2JSON to produce the JSON files for evaluation.
- TAAD + DST : you can use python run_DST_on_matches.py to run the trained TAAD+DST model on the validation or test set. It directly produce JSON files for evaluation.

Finally, use evaluation.py --predictions_file xxxxx.json --ground_truth_file yyyyy.json to generate your metrics on the validation set (the results on the test set can be evaluated using the Codabench server ==> see section Access)

---

## 📚 Citation

If the **FOOTPASS** dataset benefits your work, please kindly consider citing the paper:

```bibtex
@article{Ochin2026Footpass,
  author  = {Jérémie Ochin and Raphaël Chekroun and Bogdan Stanciulescu and Sotiris Manitsaris},
  title   = {FOOTPASS: A multi-modal multi-agent tactical context dataset for play-by-play action spotting in soccer broadcast videos},
  journal = {Computer Vision and Image Understanding},
  volume  = {269},
  pages   = {104790},
  year    = {2026},
  issn    = {1077-3142},
  doi     = {https://doi.org/10.1016/j.cviu.2026.104790},
  url     = {https://www.sciencedirect.com/science/article/pii/S1077314226001578},
}
```
You may also be interested in the following related works:

```bibtex
@InProceedings{Singh2023,
  author    = {Singh, Gurkirt and Choutas, Vasileios and Saha, Suman and Yu, Fisher and Van Gool, Luc},
  title     = {Spatio-Temporal Action Detection Under Large Motion},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  month     = {January},
  year      = {2023},
  pages     = {6009--6018}
}

@InProceedings{Ochin2025GNN,
  author    = {Ochin, Jeremie and Devineau, Guillaume and Stanciulescu, Bogdan and Manitsaris, Sotiris},
  title     = {Game State and Spatio-Temporal Action Detection in Soccer Using Graph Neural Networks and 3D Convolutional Networks},
  booktitle = {Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods (ICPRAM)},
  year      = {2025},
  pages     = {636--646},
  publisher = {SciTePress},
  organization = {INSTICC},
  doi       = {10.5220/0013161100003905},
  isbn      = {978-989-758-730-6}
}

@InProceedings{Ochin2025DST,
  author     = {Ochin, Jeremie and Chekroun, Raphael and Stanciulescu, Bogdan and Manitsaris, Sotiris},
  title      = {Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos},
  year       = {2025},
  isbn       = {978-3-032-07342-6},
  publisher  = {Springer-Verlag},
  address    = {Berlin, Heidelberg},
  url        = {https://doi.org/10.1007/978-3-032-07343-3_44},
  doi        = {10.1007/978-3-032-07343-3_44},
  booktitle  = {Advanced Concepts for Intelligent Vision Systems: 22nd International Conference, ACIVS 2025, Tokyo, Japan, July 28–30, 2025, Proceedings},
  pages      = {552–563},
  numpages   = {12},
  location   = {Tokyo, Japan}
}

@InProceedings{FeichtenhoferX3D2020,
  author    = {Feichtenhofer, Christoph},
  title     = {X3D: Expanding Architectures for Efficient Video Recognition},
  booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2020},
  pages     = {200-210},
  doi       = {10.1109/CVPR42600.2020.00028},
}

```
---

📜 License

Dataset annotations & baselines released under CC BY-NC 4.0.

Redistribution of SoccerNet broadcast videos is prohibited under the NDA.

---

✉️ Contact

Jeremie Ochin · jeremie.ochin@minesparis.psl.eu
Footovision Research · www.footovision.com

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