Name
Abstract | ||
|---|---|---|
Although marker-based optical motion capture has been a useful method for computer animation during the past decades, automatic and robust motion tracking from multiple video sequences is still very challenging. Several critical issues in practical implementations are not adequately addressed. For example, how to track and identify the reconstructed 3D points after image matching process? How to handle the heavy occlusion problem? This paper gives a careful investigation of the above issues. In particular, we propose a novel way to track and identify proper markers, and a new method of filling missing markers by taking account of the human model constraints. Experiments are presented to show its accuracy and robustness. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.433 | ICPR |
Keywords | DocType | ISSN |
motion capture, gait analysis, visual tracking,image capture,visual tracking,heavy occlusion problem,image matching,automatic gait motion capture,video sequences,motion tracking,reconstructed 3d point identification,reconstructed 3d point tracking,marker-based optical motion capture,motion estimation,image reconstruction,gait analysis,object tracking,motion capture,image matching process,missing-marker fillings,human model constraints | Conference | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Deng | 1 | 68 | 7.59 |
| Shihong Xia | 2 | 287 | 26.18 |
| Wenzhong Wang | 3 | 1 | 1.04 |
| Zhaoqi Wang | 4 | 225 | 33.91 |
| Liang Chang | 5 | 39 | 5.01 |
| Hongan Wang | 6 | 642 | 79.77 |