Multiple object tracking in video by combining neural networks within a bayesian framework
US10762644B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 13, 2018 |
| Grant date | Sep 1, 2020 |
| Priority date | — |
| Expiry date | Feb 26, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for multiple object tracking in video are described in which the outputs of neural networks are combined within a Bayesian framework. A motion model is applied to a probability distribution representing the estimated current state of a target object being tracked to predict the state of the target object in the next frame. A state of an object can include one or more features, such as the location of the object in the frame, a velocity and/or acceleration of the object across frames, a classification of the object, etc. The prediction of the state of the target object in the next frame is adjusted by a score based on the combined outputs of neural networks that process the next frame.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.