Learning the Distribution-Based Temporal Knowledge With Low Rank Response Reasoning for UAV Visual Tracking

In recent years, the constraint based correlation filter has shown good performance in unmanned aerial vehicle (UAV) tracking, which gains a lot popularity in many intelligence transportation applications. In this work, a distribution-based temporal knowledge driven method is proposed to leverage the temporal translation property in UAV tracking. Instead of focusing on the traditional issues in the correlation filter, we provide a new method of learning parametric distribution on temporal knowledge by Wasserstein distance which is successfully embedded to solve the problem of temporal degeneration in learning process of tracking. Furthermore, we approximate optimal response reasoning with low-rank constraint over response consistency. Furthermore, the proposed method is solved by a simple iterative scheme with alternating direction multiplication ADMM algorithm. We demonstrate the superior tracking performance in several public standard UAV tracking benchmarks compared with state-of-the-art algorithms.