  Particle Filter is an effective solution to track objects in video sequences
in complex situations. Its key idea is to estimate the density over the
possible states of the object using a weighted sample whose elements are called
particles. One of its crucial step is a resampling step in which particles are
resampled to avoid some degeneracy problem. In this paper, we introduce a new
resampling method called Combinatorial Resampling that exploits some features
of articulated objects to resample over an implicitly created sample of an
exponential size better representing the density to estimate. We prove that it
is sound and, through experimentations both on challenging synthetic and real
video sequences, we show that it outperforms all classical resampling methods
both in terms of the quality of its results and in terms of response times.
