Shape Tracking using Partial Information Models

One of the challenges in shape tracking is how
to deal with associating measurements to sources in the shape,
while also taking to account parameters such as shape curvature
and noise characteristics. Partial Information Models (PIMs)
introduce a new approach that addresses this issue. The idea
is to reparametrize each measurement into two components,
one which depends on the position of its source on the shape,
and another which depends on how well it fits in the shape.
This allows for the derivation of a partial likelihood which
combines the strengths of probabilistic approaches and distance
minimization techniques. We propose an implementation of
PIMs using level-sets, which allow for a close approximation
of the distribution of distances we expect for a given shape. In
turn, this can be used to develop estimators that are highly
robust against high noise and occlusions.