Low- and high-fidelity classifiers applied to road-scene images
US10373019B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 13, 2016 |
| Grant date | Aug 6, 2019 |
| Priority date | — |
| Expiry date | Sep 17, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30252
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.