Name
Abstract | ||
|---|---|---|
Tracking the articulated human body is a challenging computer vision problem because of changes in body poses and their appearance. Pictorial structure (PS) models are widely used in 2D human pose estimation. In this work, we extend the PS models for robust 3D pose estimation, which includes two stages: multi-view human body parts detection by foreground learning and pose states updating by annealed particle filter (APF) and detection. Moreover, the image dataset F-PARSE was built for foreground training and flexible mixture of parts (FMP) model was used for foreground learning. Experimental results demonstrate the effectiveness of our foreground learning-based method. |
Year | Venue | Keywords |
|---|---|---|
2014 | EUSIPCO | articulated human motion tracking,particle filtering (numerical methods),2D human pose estimation,pose states,FMP model,robust 3D pose estimation,learning (artificial intelligence),pose estimation,body poses,annealed particle filter,image dataset F-PARSE,multiview human body part detection,object tracking,object detection,pictorial structure model,computer vision,PS models,foreground training,Annealed particle filter,flexible mixture of part model,human motion tracking,computer vision problem,foreground learning,image motion analysis,APF,foreground learning-based method |
Field | DocType | ISSN |
Computer vision,3D pose estimation,Human motion,Pose,Artificial intelligence,Annealed particle filter,Articulated body pose estimation,Geography,Human body | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aichun Zhu | 1 | 16 | 8.10 |
| Hichem Snoussi | 2 | 509 | 62.19 |
| Abel Cherouat | 3 | 3 | 1.38 |