3D Localization in Ball Sports

© Universität Augsburg

Accurately tracking the ball in 3D space is crucial for sports analysis. Existing technologies, like goal-line technology in soccer, rely on expensive setups with multiple cameras. Our research explores using computer vision and machine learning to estimate the ball's 3D position in cost-effective, single-camera videos.


We focus on two promising approaches:

  • Direct 3D Prediction: Neural networks can be trained to directly estimate the ball's 3D location from a single image, considering its size and surrounding scene. While effective, this approach can be imprecise due to inherent limitations of only considering single images.
  • Physics-Guided Tracking: We can also track the ball's 3D movement across a video sequence, ensuring predictions align with the laws of physics. This method offers greater accuracy.

The Machine Learning and Computer Vision Lab investigates both techniques, with a particular interest in leveraging physical knowledge for more precise 3D ball location estimation in sports analysis.



For more information, please contact Daniel Kienzle.




© Universität Augsburg