Name
Abstract | ||
|---|---|---|
This paper investigates possible physical alterations of tracking targets to obtain improved 6DoF pose detection for a camera observing the known targets. We explore the influence of several texture characteristics on the pose detection, by simulating a large number of different target objects and camera poses. Based on statistical observations, we rank the importance of characteristics such as texturedness and feature distribution for a specific implementation of a 6DoF tracking technique. These findings allow informed modification strategies for improving the tracking target objects themselves, in the common case of man-made targets, as for example used in advertising. This fundamentally differs from and complements the traditional approach of leaving the targets unchanged while trying to optimize the tracking algorithms and parameters. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.880 | Pattern Recognition |
Keywords | Field | DocType |
cameras,feature extraction,image texture,object detection,optical tracking,pose estimation,statistical analysis,target tracking,6DoF tracking,camera pose,feature distribution,image texture,man-made target,natural feature tracking,object target tracking,optimization,pose detection,statistical observation,Natural feature tracking,simulation,tracking target optimization | Object detection,Computer vision,Natural feature tracking,Pattern recognition,Image texture,Computer science,Pose,Feature extraction,Optical tracking,Artificial intelligence,Robot,Statistical analysis | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-7542-1 | 6 |
PageRank | References | Authors |
0.68 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukas Gruber | 1 | 102 | 7.88 |
| Stefanie Zollmann | 2 | 227 | 22.58 |
| Daniel Wagner | 3 | 1274 | 85.03 |
| Dieter Schmalstieg | 4 | 4169 | 332.77 |