Increment adaptive correlation filter for visual tracking

Abstract

Currently, the correlation filter is widely used in visual tracking because of its effectiveness and efficiency. To adapt the representation to changing target appearances, a linear interpolation is used to update tracking models according to a manually designed learning rate. However, The limitation of manually tricks make methods only apply to some special scenes because the threshold parameters are sensitive to different response maps in complex scenes. In this paper, to overcome this problem, an adaptive increment correlation filter based tracker is proposed. Different from traditional linear interpolation depending on a manual learning rate, the increment is learned by linear regression based on the history tracking model and the current training samples. Experimentally, we show that our algorithm can outperform state-of-the-art key point-based trackers.