notebooks | inference | autodistill | maestro
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.
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.
We don't have a Python package yet. Install from source in a Python>=3.8 environment.
pip install git+https://github.com/roboflow/sports.git| use case | dataset |
|---|---|
| soccer player detection |  |
| soccer ball detection | |
| soccer pitch keypoint detection | |
Visit Roboflow Universe and explore other sport-related datasets.
football-ai.mp4
We love your input! Let us know what else we should build!