👋 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 environment.

pip install git+https://github.com/roboflow/sports.git

⚽ datasets

use case	dataset
soccer player detection
soccer ball detection
soccer pitch keypoint detection

Visit Roboflow Universe 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 what else we should build!