Image analysis – Applications – Target tracking or detecting
Reexamination Certificate
2006-10-26
2008-11-11
Azarian, Seyed (Department: 2624)
Image analysis
Applications
Target tracking or detecting
C382S285000, C345S474000
Reexamination Certificate
active
07450736
ABSTRACT:
Disclosed is a method and system for efficiently and accurately tracking three-dimensional (3D) human motion from a two-dimensional (2D) video sequence, even when self-occlusion, motion blur and large limb movements occur. In an offline learning stage, 3D motion capture data is acquired and a prediction model is generated based on the learned motions. A mixture of factor analyzers acts as local dimensionality reducers. Clusters of factor analyzers formed within a globally coordinated low-dimensional space makes it possible to perform multiple hypothesis tracking based on the distribution modes. In the online tracking stage, 3D tracking is performed without requiring any special equipment, clothing, or markers. Instead, motion is tracked in the dimensionality reduced state based on a monocular video sequence.
REFERENCES:
patent: 6115052 (2000-09-01), Freeman et al.
patent: 6240198 (2001-05-01), Rehg et al.
patent: 6256418 (2001-07-01), Rehg et al.
patent: 6269172 (2001-07-01), Rehg et al.
patent: 6301370 (2001-10-01), Steffens et al.
patent: 6591146 (2003-07-01), Pavlovic et al.
patent: 7148972 (2006-12-01), Rekimoto
patent: 7167578 (2007-01-01), Blake et al.
patent: 7239929 (2007-07-01), Ulrich et al.
patent: 7257237 (2007-08-01), Luck et al.
Urtasun, R., et al., “Priors for People Tracking from Small Training Sets,” Proceedings of the 10 th IEEE International Conference on Computer Vision (ICCV), 2005, pp. 403-410.
Howe, N. R., et al., “Bayesian Reconstruction of 3D Human Motion from Single-Camera Video,” Neural Information Processing Systems, 1999, 7 Pages, [online] [retrieved on Nov. 27, 2006] Retrieved from the Internet: <URL:www.ai.mit.edu/people/leventon/Research/9912-NIPS/paper.pdf>.
Belkin, M., et al., “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,” Advances in Neural Information Processing Systems (NPIS), 2001, pp. 585-591.
Brand, M., “Charting a Manifold,” Proceedings, Neural Information Processing Systems (NIPS), Dec. 2002, pp. 961-968, vol. 15.
Cham, T., et al., “A Multiple Hypothesis Approach to Figure Tracking,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 1999, pp. 239-245.
Elgammal, A., et al., “Inferring 3D Body Pose from Silhouettes Using Activity Manifold Learning,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004, pp. 681-688.
Ghahramani, Z., et al., “The EM Algorithm for Mixtures of Factor Analyzers,” Technical Report CRG-TR-96-1, University of Toronto, May 21, 1996, pp. 1-8.
Grochow, K., et al., “Style-Based Inverse Kinematics,” ACM Computer Graphics (SIGGRAPH), 2004, pp. 522-531.
Isard, M., et al., “Condensation—Conditional Density Propagation for Visual Tracking,” International Journal of Computer Vision, (IJCV), 1998, pp. 5-28, vol. 29.
Lawrence, N. D., “Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data,” Proceedings, Neural Information Processing Systems (NIPS), 2003, 8 pages.
Roweis, S., et al., “Global Coordination of Local Linear Models,” Proceedings, Neural Information Processing Systems (NIPS), 2001, pp. 889-896.
Roweis, S. T., et al., “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, Dec. 22, 2000, pp. 2323-2326, vol. 290.
Safonova, A., et al., “Synthesizing Physically Realistic Human Motion in Low-Dimensional, Behavior-Specific Spaces,” ACM Computer Graphics (SIGGRAPH), 2004, pp. 514-521.
Sidenbladh, H., et al., “Learning Image Statistics for Bayesian Tracking,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), Jul. 2001, pp. 709-716.
Sidenbladh, H., et al., “Stochastic Tracking of 3D Human Figures Using 2D Image Motion,” Proceedings, Part II of the 6thEuropean Conference on Computer Vision (ECCV), Jun./Jul. 2000, pp. 702-718.
Sigal, L., et al., “Tracking Loose-Limbed People,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004, pp. 421-428.
Sminchisescu, C., et al., “Generative Modeling for Continuous Non-Linearly Embedded Visual Inference,” Proceedings of the IEEE International Conference on Machine Learning, 2004, pp. 140-147.
Teh, Y. W., et al., “Automatic Alignment of Local Representations,” Proceedings, Neural Information Processing Systems (NIPS), 2002, pp. 841-848.
Tenenbaum, J. B., et al., “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, Dec. 22, 2000, pp. 2319-2323, vol. 290.
Tian, T., et al., “Tracking Human Body Pose on a Learned Smooth Space,” Boston University Computer Science Technical Report No. 2005-029, Aug. 2005, pp. 1-8.
Toyama, K., et al., “Probabilistic Tracking in a Metric Space,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2001, 8 pages.
Urtasun, R., et al., “Priors for People Tracking from Small Training Sets,” Proceedings of the 10thIEEE International Conference on Computer Vision (ICCV), 2005, pp. 403-410.
PCT International Search Report and Written Opinion, PCT/US/06/42135, Oct. 29, 2007, 10 pages.
Li Rui
Yang Ming-Hsuan
Azarian Seyed
Duell Mark
Fenwick & West LLP
Honda Motor Co. Ltd.
LandOfFree
Monocular tracking of 3D human motion with a coordinated... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Monocular tracking of 3D human motion with a coordinated..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Monocular tracking of 3D human motion with a coordinated... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4033325