In this paper, we propose a new adaptive visual object tracking method based on online feature evaluation approach. First, a feature set is built by combining color histogram (HC) with gradient orientation histogram (HOG), which emphasizes both color and contour representation. Then a feature confidence evaluation approach is proposed to make features with higher confidences play more important roles in the instantaneous tracking ensuring that the tracking can adapt to the appearance change of both the object and its background. The feature evaluation approach is fused with filter frameworks, e.g. Kalman and Particle filter, to keep the temporal consistency of feature confidence evolution.

中科院研究生院.pdf

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