One fundamental problem of object tracking is the
convergence of estimates to local maxima not corresponding to
target objects. To mitigate this problem, constructing a good posterior
distribution of the target state is important. In this letter, we
propose a robust tracking approach by building a new posterior
distribution model frommultiple independent estimates of a target
state. For each candidate of the target state, we compute a confidence
score based on its spatial consistency with other estimates
and photometric similarities with target models.Our posterior distribution
model reflects tracking uncertainties well and adaptively
defines the search region for the next frame. We validate the robustness
of our approach on a number of challenging datasets.