TY - JOUR
T1 - Biased manifold learning for view invariant body pose estimation
AU - Hur, Dongcheol
AU - Suk, Heung Il
AU - Wallraven, Christian
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported by WCU (World Class University) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology, under Grant R31-10008.
PY - 2012/11
Y1 - 2012/11
N2 - In human body pose estimation, manifold learning has been considered as a useful method with regard to reducing the dimension of 2D images and 3D body configuration data. Most commonly, body pose is estimated from silhouettes derived from images or image sequences. A major problem in applying manifold estimation to pose estimation is its vulnerability to silhouette variation caused by changes of factors such as viewpoint, person, and distance. In this paper, we propose a novel approach that combines three separate manifolds for viewpoint, pose, and 3D body configuration focusing on the problem of viewpoint-induced silhouette variation. The biased manifold learning is used to learn these manifolds with appropriately weighted distances. The proposed method requires four mapping functions that are learned by a generalized regression neural network for robustness. Despite the use of only three manifolds, experimental results show that the proposed method can reliably estimate 3D body poses from 2D images with all learned viewpoints.
AB - In human body pose estimation, manifold learning has been considered as a useful method with regard to reducing the dimension of 2D images and 3D body configuration data. Most commonly, body pose is estimated from silhouettes derived from images or image sequences. A major problem in applying manifold estimation to pose estimation is its vulnerability to silhouette variation caused by changes of factors such as viewpoint, person, and distance. In this paper, we propose a novel approach that combines three separate manifolds for viewpoint, pose, and 3D body configuration focusing on the problem of viewpoint-induced silhouette variation. The biased manifold learning is used to learn these manifolds with appropriately weighted distances. The proposed method requires four mapping functions that are learned by a generalized regression neural network for robustness. Despite the use of only three manifolds, experimental results show that the proposed method can reliably estimate 3D body poses from 2D images with all learned viewpoints.
KW - 3D pose estimation
KW - manifold learning
KW - nonlinear dimensionality reduction
UR - http://www.scopus.com/inward/record.url?scp=84871588370&partnerID=8YFLogxK
U2 - 10.1142/S0219691312500580
DO - 10.1142/S0219691312500580
M3 - Article
AN - SCOPUS:84871588370
SN - 0219-6913
VL - 10
JO - International Journal of Wavelets, Multiresolution and Information Processing
JF - International Journal of Wavelets, Multiresolution and Information Processing
IS - 6
M1 - 1250058
ER -