https://github.com/roboflow/sports
computer vision and sports
https://github.com/roboflow/sports
computer-vision deep-learning deep-neural-networks football football-data image-embeddings keypoint-detection object-detection soccer soccer-analytics soccer-data sports sports-analytics sports-data tutorial visualization
Last synced: 18 days ago
JSON representation
computer vision and sports
- Host: GitHub
- URL: https://github.com/roboflow/sports
- Owner: roboflow
- License: mit
- Created: 2024-05-13T14:51:16.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-19T13:31:21.000Z (10 months ago)
- Last Synced: 2025-05-15T03:04:17.750Z (18 days ago)
- Topics: computer-vision, deep-learning, deep-neural-networks, football, football-data, image-embeddings, keypoint-detection, object-detection, soccer, soccer-analytics, soccer-data, sports, sports-analytics, sports-data, tutorial, visualization
- Language: Python
- Homepage:
- Size: 4.61 MB
- Stars: 3,249
- Watchers: 65
- Forks: 386
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
sports
[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro)
## 👋 hello
In sports, every centimeter and every second matter. That's why Roboflow decided to use sports as a testing ground to push our object detection, image segmentation, keypoint detection, and foundational models to their limits. This repository contains reusable tools that can be applied in sports and beyond.
## 🥵 challenges
Are you also a fan of computer vision and sports? We welcome contributions from anyone who shares our passion! Together, we can build powerful open-source tools for sports analytics. Here are the main challenges we're looking to tackle:
- **Ball tracking:** Tracking the ball is extremely difficult due to its small size and rapid movements, especially in high-resolution videos.
- **Reading jersey numbers:** Accurately reading player jersey numbers is often hampered by blurry videos, players turning away, or other objects obscuring the numbers.
- **Player tracking:** Maintaining consistent player identification throughout a game is a challenge due to frequent occlusions caused by other players or objects on the field.
- **Player re-identification:** Re-identifying players who have left and re-entered the frame is tricky, especially with moving cameras or when players are visually similar.
- **Camera calibration:** Accurately calibrating camera views is crucial for extracting advanced statistics like player speed and distance traveled. This is a complex task due to the dynamic nature of sports and varying camera angles.
## 💻 install
We don't have a Python package yet. Install from source in a
[**Python>=3.8**](https://www.python.org/) environment.
```bash
pip install git+https://github.com/roboflow/sports.git
```
## ⚽ datasets
| use case | dataset |
|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| soccer player detection | [](https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc) |
| soccer ball detection | [](https://universe.roboflow.com/roboflow-jvuqo/football-ball-detection-rejhg) |
| soccer pitch keypoint detection | [](https://universe.roboflow.com/roboflow-jvuqo/football-field-detection-f07vi) |
Visit [Roboflow Universe](https://universe.roboflow.com/) and explore other sport-related datasets.
## 🔥 demos
https://github.com/roboflow/sports/assets/26109316/7ad414dd-cc4e-476d-9af3-02dfdf029205
## 🏆 contribution
We love your input! [Let us know](https://github.com/roboflow/sports/issues) what else we should build!