Generating labeled data for deep object tracking
US10592786B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Aug 14, 2017 |
| Grant date | Mar 17, 2020 |
| Priority date | — |
| Expiry date | Oct 12, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for generating an annotated dataset for training a deep tracking neural network, and training of the neural network using the annotated dataset. For each object in each frame of a dataset, one or more likelihood functions are calculated to correlate feature score of the object with respective feature scores each associated with one or more previously assigned target identifiers (IDs) in a selected range of frames. A target ID is assigned to the object by assigning a previously assigned target ID associated with a calculated highest likelihood or assigning a new target ID. Track management is performed according to a predefined track management scheme to assign a track type to the object. This is performed for all objects in all frames of the dataset. The resulting annotated dataset contains target IDs and track types assigned to all objects in all frames.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.