Visual Object Tracking is a very challenging task because of the large appearance variance caused by illumination, deformation, and motion. Siamese network-based trackers, which select target through a matching function, are widely used for visual object tracking. The trackers are capable of robustly recognizing the target with appearance variance. However, while the filter template is a crucial part of such methods, most of them did not update the filter template effectively, and have shown limited discriminative ability between target and similar semantic objects (distractors). In order to tackle the challenge of distractors, we added a dynamic filter branch on the traditional siamese network. Under the condition that multipeaks are detected on the static response map, the tracker will redetect target with dynamic branch and the final target location will be determined by the combined result of the dynamic filter branch and static filter branch. Subsequently the sample library with hard negative mining strategy is updated and the dynamic filter kernel is restrained online. With the fusion of two branches, the tracker can distinguish the true target from similar objects. Meanwhile, we conduct extensive experiments and empirical evaluations on two popular datasets: Visdrone and UAV123. Our tracker achieves an AUC of 58% on Visdrone dataset and an AUC of 60.7% on UAV123 dataset.

中文翻译：

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Anti-distractors：双分支连体跟踪器，带有用于对象跟踪的静态和动态过滤器

视觉对象跟踪是一项非常具有挑战性的任务，因为光照、变形和运动引起的外观差异很大。Siamese 基于网络的跟踪器通过匹配函数选择目标，广泛用于视觉对象跟踪。跟踪器能够稳健地识别具有外观差异的目标。然而，虽然过滤器模板是这些方法的关键部分，但它们中的大多数都没有有效地更新过滤器模板，并且在目标和相似语义对象（干扰器）之间的区分能力有限。为了应对干扰项的挑战，我们在传统的siamese网络上添加了一个动态过滤器分支。在静态响应图上检测到多峰的情况下，跟踪器将使用动态分支重新检测目标，最终目标位置将由动态过滤器分支和静态过滤器分支的组合结果确定。随后更新了具有硬负挖掘策略的样本库，并在线约束了动态滤波器内核。通过两个分支的融合，跟踪器可以区分真实目标和相似物体。同时，我们对两个流行的数据集进行了广泛的实验和实证评估：Visdrone 和 UAV123。我们的跟踪器在 Visdrone 数据集上实现了 58% 的 AUC，在 UAV123 数据集上实现了 60.7% 的 AUC。通过两个分支的融合，跟踪器可以区分真实目标和相似物体。同时，我们对两个流行的数据集进行了广泛的实验和实证评估：Visdrone 和 UAV123。我们的跟踪器在 Visdrone 数据集上实现了 58% 的 AUC，在 UAV123 数据集上实现了 60.7% 的 AUC。通过两个分支的融合，跟踪器可以区分真实目标和相似对象。同时，我们对两个流行的数据集进行了广泛的实验和实证评估：Visdrone 和 UAV123。我们的跟踪器在 Visdrone 数据集上实现了 58% 的 AUC，在 UAV123 数据集上实现了 60.7% 的 AUC。