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A robust algorithm solution is proposed for tracking an object in complexvideo scenes. In this solution, the bootstrap particle filter (PF) isinitialized by an object detector, which models the time-evolving background ofthe video signal by an adaptive Gaussian mixture. The motion of the object isexpressed by a Markov model, which defines the state transition prior. Thecolor and texture features are used to represent the object, and a marginallikelihood based feature fusion approach is proposed. A corresponding objecttemplate model updating procedure is developed to account for possible scalechanges of the object in the tracking process. Experimental results show thatour algorithm beats several existing alternatives in tackling challengingscenarios in video tracking tasks.

Robust video object tracking using particle filter with likelihood based feature fusion and adaptive template updating