This paper presents a novel method for reconstructing a 3D human body pose from stereo image sequences based on a top-down learning method. However, it is inefficient to build a statistical model using all training data. Therefore, the training data is hierarchically divided into several clusters to reduce the complexity of the learning problem. In the learning stage, the human body model database is hierarchically constructed by classifying the training data into several sub-clusters with silhouette images. The data of each cluster in the bottom level is represented by a linear combination of examples. In the reconstruction stage, the proposed method hierarchically searches a cluster for the best matching silhouette image using a silhouette history image (SHI). Then, the 3D human body pose is reconstructed from a depth image using a linear combination of examples method. By using depth information to reconstruct 3D human body pose, the similar poses in silhouette images are estimated as different 3D human body poses. The experimental results demonstrate that the proposed method is efficient and effective for reconstructing 3D human body poses.
Bibliographical noteFunding Information:
The authors are grateful to anonymous reviewers for helpful comments that have improved the article. This research was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2005-041-D00724).
- 3D human modeling
- Depth information
- Reconstruction of 3D human body pose
- Spatio-temporal features
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence