Target tracking with inter-supervised convolutional networks
US10204288B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Apr 13, 2017 |
| Grant date | Feb 12, 2019 |
| Priority date | — |
| Expiry date | Apr 27, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/62
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
We propose a tracking framework that explicitly encodes both generic features and category-based features. The tracker consists of a shared convolutional network (NetS), which feeds into two parallel networks, NetC for classification and NetT for tracking. NetS is pre-trained on ImageNet to serve as a generic feature extractor across the different object categories for NetC and NetT. NetC utilizes those features within fully connected layers to classify the object category. NetT has multiple branches, corresponding to multiple categories, to distinguish the tracked object from the background. Since each branch in NetT is trained by the videos of a specific category or groups of similar categories, NetT encodes category-based features for tracking. During online tracking, NetC and NetT jointly determine the target regions with the right category and foreground labels for target estimation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.