Proxy training data for human body tracking
US8213680B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 19, 2010 |
| Grant date | Jul 3, 2012 |
| Priority date | — |
| Expiry date | Feb 11, 2031 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Synthesized body images are generated for a machine learning algorithm of a body joint tracking system. Frames from motion capture sequences are retargeted to several different body types, to leverage the motion capture sequences. To avoid providing redundant or similar frames to the machine learning algorithm, and to provide a compact yet highly variegated set of images, dissimilar frames can be identified using a similarity metric. The similarity metric is used to locate frames which are sufficiently distinct, according to a threshold distance. For realism, noise is added to the depth images based on noise sources which a real world depth camera would often experience. Other random variations can be introduced as well. For example, a degree of randomness can be added to retargeting. For each frame, the depth image and a corresponding classification image, with labeled body parts, are provided. 3-D scene elements can also be provided.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.