  A robust algorithm solution is proposed for tracking an object in complex
video scenes. In this solution, the bootstrap particle filter (PF) is
initialized by an object detector, which models the time-evolving background of
the video signal by an adaptive Gaussian mixture. The motion of the object is
expressed by a Markov model, which defines the state transition prior. The
color and texture features are used to represent the object, and a marginal
likelihood based feature fusion approach is proposed. A corresponding object
template model updating procedure is developed to account for possible scale
changes of the object in the tracking process. Experimental results show that
our algorithm beats several existing alternatives in tackling challenging
scenarios in video tracking tasks.
