论文标题
alpha-refine：通过精确的边界箱估计来提高跟踪性能
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
论文作者
论文摘要
近年来，多阶段策略已成为视觉跟踪的流行趋势。该策略首先利用基本跟踪器将目标定位，然后利用改进模块以获得更准确的结果。但是，现有的改进模块遭受有限的可传递性和精度。在这项工作中，我们提出了一个称为alpha-refine的新颖，柔性和准确的改进模块，该模块利用精确的像素的相关层以及空间感知的非本地层来融合特征，可以预测三个互补的输出：边界，角落，拐角和面罩。要明智地选择最足够的输出，我们还设计了一个轻巧的分支选择器模块。我们将拟议的α-refine模块应用于五个著名和最先进的基本跟踪器：DIMP，Atom，Siamrpn ++，RTMDNET和ECO。关于TrackingNet，Lasot和fot2018基准的综合实验表明，与其他现有改进方法相比，我们的方法可显着提高跟踪性能。源代码将在https://github.com/masterbin-iiau/alpharefine上找到。
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 https://github.com/MasterBin-IIAU/AlphaRefine.
