Fusion of visual and non-visual information for training deep learning models
US10867217B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 4, 2018 |
| Grant date | Dec 15, 2020 |
| Priority date | — |
| Expiry date | Mar 12, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/10
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method includes obtaining data from one or more non-visual sensors and a camera from a first monitoring system. The data includes non-visual data from the non-visual sensors and visual data obtained from the camera. The non-visual data from the non-visual sensors are paired with corresponding visual data from the camera. Data points of the non-visual data are synchronized with frames of the visual data based on a likelihood of an event indicated in the non-visual data. The synchronized data points of the non-visual data with the frames of the visual data are provided as labeled input to a neural network to train the neural network to detect the event. The trained neural network is provided to one or more cameras corresponding to one or more additional monitoring systems to detect the event in the visual data obtained by the one or more cameras.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.