Accurate appearance-based
Bayesian tracking for maneuvering targets
This page presents the results of a
tracking algorithm based on a mean shift search in a particle
filtering framework and on a target representation that uses
multiple semi-overlapping color histograms. The target
representation introduces spatial information that accounts for
rotation and anisotropic scaling without compromising the
flexibility typical of color histograms. Moreover, the proposed
tracker can generate a smaller number of samples than particle
filter as it increases the particle efficiency by moving the
samples toward a close local maximum of the likelihood using
mean shift. Experimental results show that the proposed
representation improves the robustness to clutter and that,
especially on highly maneuvering targets, the combined tracker
outperforms particle filter and mean shift in terms of accuracy
in estimating the target size and position while generating only
25% of the samples used by particle filter.
Original test sequences with ground truth data
are also provided.