An online combined feature evaluation method for visual object tracking is put forward in this paper. Firstly, a feature
set is built by combining color histogram (HC) bins with gradient orientation histogram (HOG) bins to emphasize the
color representation and contour representation of an object respectively. Then a feature confidence evaluation approach
in a Particle Filter framework is proposed to make that features of larger confidence can play more important roles in the
instantaneous tracking, ensuring that the tracking can adapt to the appearance changes of either foreground or
background. In this way, we extend the traditional filter framework from modeling motion states to modeling feature
evaluation. The temporal consistency of particles can also ensure that the evolution of feature confidence is always
gentle. Examples are presented to illustrate how the method adapts to changing appearances of both tracked object and
background. Experiments and comparisons demonstrate that object tracking with evaluated combined features are highly
reliable even when objects go across complex backgrounds.
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