Nvidia patents camera calibration for sports fields using self-learning
Nvidia describes a method that uses machine learning to automatically calibrate cameras positioned over sports fields. The system first learns to recognize general field markings and then fine-tunes itself using specific images of a particular field to improve accuracy in detecting lines and features.
In the filing’s own words
In various examples, techniques for self-supervised learning for real-time calibration of dynamic image sensors is described herein. Systems and methods described herein may initially train one or more machine learning models to perform camera calibration with regard to a surface layout for a type of sport. For instance, synthetic training data representing synthetic images and/or real-world training data representing real-world images depicting sporting surfaces associated with the surface layout may be used to train the model(s). Systems and methods described herein may then perform self-supervised fine-tuning of the model(s) for a specific sporting surface that is also associated with the surface layout. For instance, image data representing images of the sporting surface, along with a reference surface that indicates at least dimensions and/or feature locations of the surface layout, may be used to fine-tune the model(s) to be more accurate at feature detection for the sporting surface.
What exactly they claim (claim 1)
1. A method of fine-tuning one or more machine learning models, comprising: obtaining image data obtained using one or more image sensors and representative of one or more images depicting a statically-positioned surface comprising one or more visual demarcations; determining, using the one or more machine learning models and based at least on the image data, one or more first feature points associated with at least an image of the one or more images; determining, based at least on the one or more first feature points, one or more values for one or more calibration parameters corresponding to at least one image sensor of the one or more image sensors; determining, based at least on the one or more values and a reference layout associated with the surface, one or more second feature points associated with the image; and updating, based at least on the one or more first feature points and the one or more second feature points, one or more parameters of the one or more machine learning models to generate one or more fine-tuned machine learning models.
Facts
- Company
- Nvidia (NVIDIA Corporation)
- Official title
- SELF-SUPERVISED LEARNING FOR REAL-TIME CALIBRATION OF DYNAMIC IMAGE SENSORS
- Publication number
- US20260330294A1
- Application number
- 19183138
- Filed
- Apr 18, 2025
- Published
- Oct 22, 2026
- Inventors
- 2
- Claims
- 20
Classified under
G06N 20/00 Physics & computingG06T 7/80 Physics & computingG06V 10/44 Physics & computingMore from Nvidia in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.