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Abstract

Interacting and annealing are two powerful strategies
that are applied in different areas of stochastic modelling
and data analysis. Interacting particle systems approximate
a distribution of interest by a finite number of particles
where the particles interact between the time steps. In computer
vision, they are commonly known as particle filters.
Simulated annealing, on the other hand, is a global optimization
method derived from statistical mechanics. A recent
heuristic approach to fuse these two techniques for motion
capturing has become known as annealed particle filter.
In order to analyze these techniques, we rigorously derive in
this paper two algorithms with annealing properties based
on the mathematical theory of interacting particle systems.
Convergence results and sufficient parameter restrictions enable
us to point out limitations of the annealed particle filter.
Moreover, we evaluate the impact of the parameters on the
performance in various experiments, including the tracking
of articulated bodies from noisy measurements. Our results
provide a general guidance on suitable parameter choices for
different applications.