Visual tracking for UAV using adaptive spatio-temporal regularized correlation filters

摘要

The advance of visual tracking has provided unmanned aerial vehicle (UAV) with the intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter (DCF)-based trackers have drawn significant attention and undergone remarkable progress. However, the boundary effect and filter degradation remain two intractable problems. In this work, we propose a novel Adaptive Spatio-Temporal Regularized Correlation Filters (ASTR-CF) model to address the two problems. The ASTR-CF model simultaneously optimizes the spatial and temporal regularization weights adaptively, and it is optimized by the alternating direction method of multipliers (ADMM) effectively. Extensive experiments on 4 UAV tracking benchmarks have proven the superiority of the proposed ASTR-CF compared with more than 30 state-of-the-art trackers in terms of accuracy and speed.