Method of using deep discriminate network model for person re-identification in image or video
US11100370B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 23, 2018 |
| Grant date | Aug 24, 2021 |
| Priority date | — |
| Expiry date | May 25, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed is a deep discriminative network for person re-identification in an image or a video. Concatenation are carried out on different input images on a color channel by constructing a deep discriminative network, and an obtained splicing result is defined as an original difference space of different images. The original difference space is sent into a convolutional network. The network outputs the similarity between two input images by learning difference information in the original difference space, thereby realizing person re-identification. The features of an individual image are not learnt, and concatenation are carried out on input images on a color channel at the beginning, and difference information is learnt on an original space of the images by using a designed network. By introducing an Inception module and embedding the same into a model, the learning ability of a network can be improved, and a better differentiation effect can be achieved.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.