|
Published Articles >> Table of Contents >> Abstract
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 1
pp. 421-428
Tracking Loose-Limbed People
Leonid Sigal, Brown University
Sidharth Bhatia, Brown University
Stefan Roth, Brown University
Michael J Black, Brown University
Michael Isard, Microsoft Research Silicon Valley
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.252
Send link to a friend
| Abstract |
|
We pose the problem of 3D human tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected limbs. Conditional probabilities relating the 3D pose of connected limbs are learned from motion-captured training data. Similarly, we learn probabilistic models for the temporal evolution of each limb (forward and backward in time). Human pose and motion estimation is then solved with non-parametric belief propagation using a variation of particle filtering that can be applied over a general loopy graph. The loose-limbed model and decentralized graph structure facilitate the use of low-level visual cues. We adopt simple limb and head detectors to provide "bottom-up" information that is incorporated into the inference process at every time-step; these detectors permit automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking a walking person in video imagery using four calibrated cameras. Our experimental apparatus includes a marker-based motion capture system aligned with the coordinate frame of the calibrated cameras with which we quantitatively evaluate the accuracy of our 3D person tracker.
|
 Additional Information
|
Citation:
Leonid Sigal, Sidharth Bhatia, Stefan Roth, Michael J Black, Michael Isard,
"Tracking Loose-Limbed People,"
cvpr,
pp. 421-428,
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 1,
2004
|
|
