Semi-supervised action-actor detection from tracking data in sport
US12100244B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 27, 2021 |
| Grant date | Sep 24, 2024 |
| Priority date | — |
| Expiry date | May 6, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system of generating agent and actions prediction based on multi-agent tracking data are disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a trained neural network by generating a plurality of training data sets based on the tracking data by converting each frame of data into a matrix representation of the data contained in the frame and learning, by the neural network, a start frame and end frame of each action contained in the frame and its associated actor. The computing system receives target tracking data associated with an event. The target tracking data includes a plurality of actors and a plurality of actions. The computing system generates, via the trained neural network, a target start frame and a target end frame of each action identified in the tracking data and a corresponding actor.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.