论文标题
海洋：无锚锚的跟踪
Ocean: Object-aware Anchor-free Tracking
论文作者
论文摘要
基于锚的暹罗跟踪器在准确性方面取得了显着进步，但进一步的进步受到滞后跟踪鲁棒性的限制。我们发现的根本原因是，基于锚的方法中的回归网络仅在正锚盒上训练（即$ iou \ geq0.6 $）。这种机制使得很难完善其与目标对象重叠的锚固剂。在本文中，我们提出了一个新颖的无锚锚网络来解决这个问题。首先，我们没有完善参考锚盒，而是直接以无锚方式进行目标对象的位置和规模。由于训练有素的地面图盒中的每个像素，因此跟踪器能够纠正推断过程中目标对象的不精确预测。其次，我们引入了一个功能对齐模块，以从预测的边界框中学习一个对象感知功能。对象感知功能可以进一步有助于目标对象和背景的分类。此外，我们提出了一个基于无锚模型的新颖跟踪框架。实验表明，我们的无锚追踪器在五个基准测试中实现了最先进的性能，包括2018年的Fot-2018，Fot-2019，OTB-100，OTB-100，GOT-10K和LASOT。源代码可在https://github.com/researchmm/trackit上找到。
Anchor-based Siamese trackers have achieved remarkable advancements in accuracy, yet the further improvement is restricted by the lagged tracking robustness. We find the underlying reason is that the regression network in anchor-based methods is only trained on the positive anchor boxes (i.e., $IoU \geq0.6$). This mechanism makes it difficult to refine the anchors whose overlap with the target objects are small. In this paper, we propose a novel object-aware anchor-free network to address this issue. First, instead of refining the reference anchor boxes, we directly predict the position and scale of target objects in an anchor-free fashion. Since each pixel in groundtruth boxes is well trained, the tracker is capable of rectifying inexact predictions of target objects during inference. Second, we introduce a feature alignment module to learn an object-aware feature from predicted bounding boxes. The object-aware feature can further contribute to the classification of target objects and background. Moreover, we present a novel tracking framework based on the anchor-free model. The experiments show that our anchor-free tracker achieves state-of-the-art performance on five benchmarks, including VOT-2018, VOT-2019, OTB-100, GOT-10k and LaSOT. The source code is available at https://github.com/researchmm/TracKit.
