Tracking Loose-limbed People
Leonid Sigal, Sidharth Bhatia, Stefan Roth, Michael J Black and
Michael Isard
Proc. Computer Vision and Pattern Recognition, vol. 1 421-428. (2004)
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.
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