Smooth Incremental Learning of Correlation Filters for Visual Tracking

Abstract

Correlation Filters (CFs) are widely applied to visual tracking because of their effectiveness and efficiency. However, the online learning of CFs is problematic because of the increasing number of training samples as the tracking process goes on. To solve this issue, most CFs-based methods decouple the learning phase and updating phase of the correlation filters and then use a simple linear interpolation to fuse the newly learned correlation filters with the old ones for fast computing. Nevertheless, the linear interpolation may be an unwise way to update the model. In this letter, we propose a smooth incremental learning framework of CFs. In our method, the increments of the correlation filters are smoothly learned by minimizing an optimization problem in each frame, thus avoiding the ad hoc linear interpolation. The optimization problem can be efficiently solved via the Alternating Direction Method of Multipliers (ADMM). Furthermore, a rotation estimation strategy is introduced to enable the correlation filters to accurately estimate the rotation angle of the target. Experiments show the superiority of the incremental learning framework and the rotation estimation method.