  In this article, we are concerned with tracking an object of interest in
video stream. We propose an algorithm that is robust against occlusion, the
presence of confusing colors, abrupt changes in the object feature space and
changes in object size. We develop the algorithm within a Bayesian modeling
framework. The state space model is used for capturing the temporal correlation
in the sequence of frame images by modeling the underlying dynamics of the
tracking system. The Bayesian model averaging (BMA) strategy is proposed for
fusing multi-clue information in the observations. Any number of object
features are allowed to be involved in the proposed framework. Every feature
represents one source of information to be fused and is associated with an
observation model. The state inference is performed by employing the particle
filter methods. In comparison with related approaches, the BMA based tracker is
shown to have robustness, expressivity, and comprehensibility.
