Classifying time series image data
US11017296B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 22, 2018 |
| Grant date | May 25, 2021 |
| Priority date | — |
| Expiry date | Feb 1, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.