Name
Abstract | ||
|---|---|---|
State-of-the-art methods for articulated human pose estimation are based on pictorial structures model (PS). Most of these methods predict the pose directly in part-based models and only consider rigid parts guided by human anatomy. In this paper, we propose a new framework for human pose estimation which is composed of two stages: pre-estimation and estimation. The first stage includes three steps: upper body detection, upper body categorization, and model selection. In the second stage, a new upper body category based multiple mixture parts (MMP) model is proposed. We present quantitative results demonstrating that our model significantly improves the accuracy of the pose estimation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/AVSS.2015.7301801 | 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
Keywords | Field | DocType |
articulated pose estimation,articulated human pose estimation,pictorial structures model,human anatomy,upper body detection,upper body categorization,model selection,upper body category based multiple mixture parts model | Categorization,Computer vision,Pattern recognition,Computer science,3D pose estimation,Model selection,Pose,Artificial intelligence,Articulated body pose estimation,Human anatomy | Conference |
Citations | PageRank | References |
1 | 0.34 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aichun Zhu | 1 | 1 | 0.34 |
| Hichem Snoussi | 2 | 509 | 62.19 |
| Abel Cherouat | 3 | 3 | 1.38 |