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
视觉对象跟踪旨在精确估计给定目标的边界框，这是一个充满挑战的问题，这是由于变形和遮挡等因素。许多最近的跟踪器采用多阶段跟踪策略来提高边界框估计的质量。这些方法首先将目标定位，然后在以下阶段完善初始预测。但是，现有方法仍然受到有限的精度，不同阶段的耦合严重限制了该方法的可转移性。这项工作提出了一个称为α-雷丁（AR）的新颖，灵活和准确的改进模块，该模块可以显着提高基本跟踪器的盒子估计质量。通过探索一系列设计选项，我们得出结论，成功改进的关键是尽可能尽可能地提取和维护详细的空间信息。遵循这一原则，阿尔法 - 雷替恩采用像素的相关性，角度预测头和辅助面具头作为核心组件。具有多个基本跟踪器的Trackingnet，Lasot，GOT-10K和Dot2020基准测试的全面实验表明，我们的方法大大提高了基本跟踪器的性能，几乎没有额外的延迟。拟议的α练习方法导致了一系列增强的跟踪器，其中ArsiamRPN（AR增强的siamrpnpp）和Ardimp50（Arstrengented DIMP50）实现了良好的效率优先折衷，而ArdimpSuperper（AR增强的DIMP-SUPEPER）在实时的速度上实现了非常有竞争力的速度。代码和预估计的模型可在https://github.com/masterbin-iiau/alpharefine上找到。
Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage tracking strategy to improve the quality of bounding box estimation. These methods first coarsely locate the target and then refine the initial prediction in the following stages. However, existing approaches still suffer from limited precision, and the coupling of different stages severely restricts the method's transferability. This work proposes a novel, flexible, and accurate refinement module called Alpha-Refine (AR), which can significantly improve the base trackers' box estimation quality. By exploring a series of design options, we conclude that the key to successful refinement is extracting and maintaining detailed spatial information as much as possible. Following this principle, Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an auxiliary mask head as the core components. Comprehensive experiments on TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers show that our approach significantly improves the base trackers' performance with little extra latency. The proposed Alpha-Refine method leads to a series of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp) and the ARDiMP50 (ARstrengthened DiMP50) achieve good efficiency-precision trade-off, while the ARDiMPsuper (AR strengthened DiMP-super) achieves very competitive performance at a real-time speed. Code and pretrained models are available at https://github.com/MasterBin-IIAU/AlphaRefine.
