Learning to track 3D human motion from silhouettes
Ankur Agarwal and William Triggs
In: ICML 2004, 04-08 Jul 2004, Banff, Canada.
We describe a sparse Bayesian regression method for recovering 3D
human body motion directly from silhouettes extracted from monocular
video sequences. No detailed body shape model is needed, and realism
is ensured by training on real human motion capture data. The tracker
estimates 3D body pose by using Relevance Vector Machine regression to
combine a learned autoregressive dynamical model with robust shape
descriptors extracted automatically from image silhouettes. We
studied several different combination methods, the most effective
being to learn a nonlinear observation-update correction based on
joint regression with respect to the predicted state and the
observations. We demonstrate the method on a 54-parameter full body
pose model, both quantitatively using motion capture based test
sequences, and qualitatively on a test video sequence.