View-Point Insensitive Human Pose Recognition using Neural Network
This paper proposes view-point insensitive human
pose recognition system using neural network. Recognition system
consists of silhouette image capturing module, data driven database,
and neural network. The advantages of our system are first, it is
possible to capture multiple view-point silhouette images of 3D human
model automatically. This automatic capture module is helpful to
reduce time consuming task of database construction. Second, we
develop huge feature database to offer view-point insensitivity at pose
recognition. Third, we use neural network to recognize human pose
from multiple-view because every pose from each model have similar
feature patterns, even though each model has different appearance and
view-point. To construct database, we need to create 3D human model
using 3D manipulate tools. Contour shape is used to convert silhouette
image to feature vector of 12 degree. This extraction task is processed
semi-automatically, which benefits in that capturing images and
converting to silhouette images from the real capturing environment is
needless. We demonstrate the effectiveness of our approach with
experiments on virtual environment.
[1] Y. Sagawa, M. Shimosaka, T. Mori and T. Sato, "Fast Online Human Pose
Estimation via 3D Voxel Data", Intelligent Robots and Systems, pp.
1034-1040, 2007.
[2] Catherine A., Xingtai Q., Arash M., Maurice., "A novel approach for
recognition of human actions", Machine Vision and Applications, 2008,
pp. 27-34, 2008.
[3] M. Voit, K. Nickel, R. Stiefelhagen, "Neural Network-Based Head Pose
Estimation and Multi-view Fusion", LNCS 4122, pp. 291-298, 2007.
[4] C. Yuan, H. Niemann, "Neural networks for the recognition and pose
estimation of 3D objects from a single 2D perspective view", Image and
Vision Computing 19, pp. 585-592, 2001.
[5] URL downloads 3D models : http://www.turbosquid.com
[6] A. Agarwal, B. Triggs, "Human Pose from Silhouettes by Relevance
Vector Regression", CVPR, vol2., pp882-888, 2004.
[7] R. Rosales, S. Sclaroff, "Learning and synthesizing human body motion
and posture", IEEE, 2000.
[8] F. Lb, R. Nevatia, "Single View Human Action Recognition using Key
Pose Matching and Viterbi Path Searching", CVPR, 2007.
[9] H. Yu, G. Sun, W. Song, X. Li, "Human Motion Recognition Based on
Neural Network", IEEE, Vol. 2, pp. 982, 2005.
[10] M. Voit, K. Nicket, R. Stiefelhagen, "Multi-view Head Pose Estimation
using Neural Networks", Computer and Robot Vision, pp. 347-352, 2005.
[1] Y. Sagawa, M. Shimosaka, T. Mori and T. Sato, "Fast Online Human Pose
Estimation via 3D Voxel Data", Intelligent Robots and Systems, pp.
1034-1040, 2007.
[2] Catherine A., Xingtai Q., Arash M., Maurice., "A novel approach for
recognition of human actions", Machine Vision and Applications, 2008,
pp. 27-34, 2008.
[3] M. Voit, K. Nickel, R. Stiefelhagen, "Neural Network-Based Head Pose
Estimation and Multi-view Fusion", LNCS 4122, pp. 291-298, 2007.
[4] C. Yuan, H. Niemann, "Neural networks for the recognition and pose
estimation of 3D objects from a single 2D perspective view", Image and
Vision Computing 19, pp. 585-592, 2001.
[5] URL downloads 3D models : http://www.turbosquid.com
[6] A. Agarwal, B. Triggs, "Human Pose from Silhouettes by Relevance
Vector Regression", CVPR, vol2., pp882-888, 2004.
[7] R. Rosales, S. Sclaroff, "Learning and synthesizing human body motion
and posture", IEEE, 2000.
[8] F. Lb, R. Nevatia, "Single View Human Action Recognition using Key
Pose Matching and Viterbi Path Searching", CVPR, 2007.
[9] H. Yu, G. Sun, W. Song, X. Li, "Human Motion Recognition Based on
Neural Network", IEEE, Vol. 2, pp. 982, 2005.
[10] M. Voit, K. Nicket, R. Stiefelhagen, "Multi-view Head Pose Estimation
using Neural Networks", Computer and Robot Vision, pp. 347-352, 2005.
@article{"International Journal of Information, Control and Computer Sciences:53646", author = "Sanghyeok Oh and Yunli Lee and Kwangjin Hong and Kirak Kim and Keechul Jung", title = "View-Point Insensitive Human Pose Recognition using Neural Network", abstract = "This paper proposes view-point insensitive human
pose recognition system using neural network. Recognition system
consists of silhouette image capturing module, data driven database,
and neural network. The advantages of our system are first, it is
possible to capture multiple view-point silhouette images of 3D human
model automatically. This automatic capture module is helpful to
reduce time consuming task of database construction. Second, we
develop huge feature database to offer view-point insensitivity at pose
recognition. Third, we use neural network to recognize human pose
from multiple-view because every pose from each model have similar
feature patterns, even though each model has different appearance and
view-point. To construct database, we need to create 3D human model
using 3D manipulate tools. Contour shape is used to convert silhouette
image to feature vector of 12 degree. This extraction task is processed
semi-automatically, which benefits in that capturing images and
converting to silhouette images from the real capturing environment is
needless. We demonstrate the effectiveness of our approach with
experiments on virtual environment.", keywords = "Computer vision, neural network, pose recognition,view-point insensitive.", volume = "2", number = "8", pages = "2655-4", }