Learning Correlation Filter With Detection Response For Visual Tracking

Correlation Filter (CF) has been a powerful tool for real-time visual object tracking. Although enormous improvements have been made since the first CF tracker was proposed, most of the existing CF tracking schemes adopt a fixed Gaussian label to train correlation filter. We argue that it is not optimal for challenging scenarios. Additionally, existing training label managing strategies are too complex to maintain the speed advantage of CF algorithms. In this work, we propose a novel label to supervise filter training. Our method is capable of being integrated into most CF trackers conveniently without increasing computational complexity. We employ the proposed scheme for four existing CF trackers and conduct extensive experiments on popular benchmark. The encouraging experimental results validate both the effi-ciency and efficacy of our proposed method. What's more, we provide a detailed analysis for challenging applications and hyperparameter setting.