Name
Abstract | ||
|---|---|---|
This paper presents a method for tracking human poses in real-time from depth image sequences. The key idea is to adopt recognition for generating the model to be tracked. In contrast to traditional methods utilizing a single-typed 3D body model, we directly define the human body model based on the body part recognition result of the captured depth image, which leads to the reliable tracking regardless of users' appearances. Moreover, the proposed method has the ability to efficiently reduce the tracking drift by exploiting the joint information inserted into our body model. Experimental results on real-world environments show that the proposed method is effective for estimating various human poses in real-time. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/12.2037644 | VISUAL INFORMATION PROCESSING AND COMMUNICATION V |
Keywords | Field | DocType |
Pose tracking, depth image sequences, body part recognition, tracking drift | Computer vision,Pose tracking,Tracking system,Artificial intelligence,Human body,Physics | Conference |
Volume | ISSN | Citations |
9029 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wonjun Kim | 1 | 301 | 26.50 |
| ByungIn Yoo | 2 | 36 | 5.52 |
| Jae-Joon Han | 3 | 18 | 1.40 |
| Changkyu Choi | 4 | 159 | 14.99 |