  Multiple Instance Learning (MIL) recently provides an appealing way to
alleviate the drifting problem in visual tracking. Following the
tracking-by-detection framework, an online MILBoost approach is developed that
sequentially chooses weak classifiers by maximizing the bag likelihood. In this
paper, we extend this idea towards incorporating the instance significance
estimation into the online MILBoost framework. First, instead of treating all
instances equally, with each instance we associate a significance-coefficient
that represents its contribution to the bag likelihood. The coefficients are
estimated by a simple Bayesian formula that jointly considers the predictions
from several standard MILBoost classifiers. Next, we follow the online boosting
framework, and propose a new criterion for the selection of weak classifiers.
Experiments with challenging public datasets show that the proposed method
outperforms both existing MIL based and boosting based trackers.
