Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result. Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve state-of-the-art results. The code will be available at https://github.com/hqucv/siamrn

本文介绍一种在复杂场景下提高视觉跟踪器准确性的新型Siamese relation network，包括两个有效模块：Relation Detector（RD）和Refinement Module（RM），可实现从背景中过滤干扰器并生成跟踪结果。此外，引入了对比训练策略，使跟踪器具有区分不同物体的能力，实验表明该方法可在背景干扰、快速运动和遮挡等情况下取得最先进的结果。

学习过滤：用孪生关系网络进行鲁棒跟踪