Online feature evaluation for object tracking using Kalman Filter

An online feature evaluation method for visual object tracking is put forward in this paper. Firstly, a combined feature set is built using color histogram (HC) bins and gradient orientation histogram (HOG) bins considering the color and contour representation of an object respectively. Then a novel method is proposed to evaluate the features¿ weights in a tracking process using Kalman Filter, which is used to comprise the inter-frame predication and single-frame measurement of features¿ discriminative power. In this way, we extend the traditional filter framework from modeling motion states to modeling feature evaluation. Experiments show this method can greatly improve the tracking stabilization when objects go across complex backgrounds.