19 January 2009 Feature evaluation by particle filter for adaptive object tracking
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Abstract
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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Zhenjun Han, Qixiang Ye, Yanmei Liu, Jianbin Jiao, "Feature evaluation by particle filter for adaptive object tracking", Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72571G (19 January 2009); doi: 10.1117/12.805558; https://doi.org/10.1117/12.805558
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