We present an articulated tracking system working with data
from a single narrow baseline stereo camera. The use of stereo data allows
for some depth disambiguation, a common issue in articulated tracking,
which in turn yields likelihoods that are practically unimodal. While current
state-of-the-art trackers utilize particle filters, our unimodal likelihood
model allows us to use an unscented Kalman filter. This robust
and efficient filter allows us to improve the quality of the tracker while
using substantially fewer likelihood evaluations. The system is compared
to one based on a particle filter with superior results. Tracking quality
is measured by comparing with ground truth data from a marker-based
motion capture system.