Learning and tracking cyclic human motion
2001
Conference Paper
ps
We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data automatically into "cycles". Then the mean and the principal components of the cycles are computed using a new algorithm that accounts for missing information and enforces smooth transitions between cycles. The learned temporal model provides a prior probability distribution over human motions that can be used in a Bayesian framework for tracking human subjects in complex monocular video sequences and recovering their 3D motion.
Author(s): | Ormoneit, D. and Sidenbladh, H. and Black, M. J. and Hastie, T. |
Book Title: | Advances in Neural Information Processing Systems 13, NIPS |
Pages: | 894-900 |
Year: | 2001 |
Editors: | Leen, Todd K. and Dietterich, Thomas G. and Tresp, Volker |
Publisher: | The MIT Press |
Department(s): | Perceiving Systems |
Bibtex Type: | Conference Paper (inproceedings) |
Paper Type: | Conference |
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BibTex @inproceedings{Black:MIT:2001, title = {Learning and tracking cyclic human motion}, author = {Ormoneit, D. and Sidenbladh, H. and Black, M. J. and Hastie, T.}, booktitle = {Advances in Neural Information Processing Systems 13, NIPS}, pages = {894-900}, editors = {Leen, Todd K. and Dietterich, Thomas G. and Tresp, Volker}, publisher = {The MIT Press}, year = {2001}, doi = {} } |