Implement a generalized tracking framework
that processes relevant image sequences in "easiest-first" order.
The algorithm hypothesizes moving rigid objects to explain image
changes. Each object hypothesis includes an appearance model
(i.e., segmented views) and spatiotemporal locations for the object.
These object hypotheses form the second level of the hierarchy.
When an image sequence cannot be adequately explained by the current
set of objects and object trajectories, the algorithm will add objects,
split objects, or remove objects in an effort to better explain the
sequence. Tracking decisions are put off for as long as is feasible
by tracking multiple hypotheses. The easiest correspondence problems
are solved first because the information gained by solving them may
help to explain more difficult tracking instances.