In recent years, the multiple-stage strategy has become a popular trend for
visual tracking. This strategy first utilizes a base tracker to coarsely locate
the target and then exploits a refinement module to obtain more accurate
results. However, existing refinement modules suffer from the limited
transferability and precision. In this work, we propose a novel, flexible and
accurate refinement module called Alpha-Refine, which exploits a precise
pixel-wise correlation layer together with a spatial-aware non-local layer to
fuse features and can predict three complementary outputs: bounding box,
corners and mask. To wisely choose the most adequate output, we also design a
light-weight branch selector module. We apply the proposed Alpha-Refine module
to five famous and state-of-the-art base trackers: DiMP, ATOM, SiamRPN++,
RTMDNet and ECO. The comprehensive experiments on TrackingNet, LaSOT and
VOT2018 benchmarks demonstrate that our approach significantly improves the
tracking performance in comparison with other existing refinement methods. The
source codes will be available at
this https URL

本文介绍了一种新的、灵活的和准确的细化模块 Alpha-Refine，并将其应用于五种基础跟踪器中，实现了在 TrackingNet、LaSOT 和 VOT2018 基准测试中比其他现有的细化方法显著提高跟踪性能的效果。