  Recently, some correlation filter based trackers with detection proposals
have achieved state-of-the-art tracking results. However, a large number of
redundant proposals given by the proposal generator may degrade the performance
and speed of these trackers. In this paper, we propose an adaptive proposal
selection algorithm which can generate a small number of high-quality proposals
to handle the problem of scale variations for visual object tracking.
Specifically, we firstly utilize the color histograms in the HSV color space to
represent the instances (i.e., the initial target in the first frame and the
predicted target in the previous frame) and proposals. Then, an adaptive
strategy based on the color similarity is formulated to select high-quality
proposals. We further integrate the proposed adaptive proposal selection
algorithm with coarse-to-fine deep features to validate the generalization and
efficiency of the proposed tracker. Experiments on two benchmark datasets
demonstrate that the proposed algorithm performs favorably against several
state-of-the-art trackers.
