Single object tracking in visual media is an important yet challenging task. Various challenges, especially target scale variation, shape deformation and occlusion, can have large effects on the performances of trackers. Current deep regression based trackers only pay close attention to regression on the center key point of the tracking target, meanwhile employ the image pyramid based multi-scale testing method to deal with scale estimation. Such procedure can not properly handle the three challenges. We address these challenges in a principled way by the aid of auxiliary regressions on the four bounding box corners of the tracking target. In this work, we propose the novel Corner Aided Tracker with deep regression network, abbreviated as CAT. Different from RPN-based trackers, in CAT, four corners along with the center key point of the bounding box for tracking target are simultaneously obtained by five corresponding response maps. Furthermore, to robustly and accurately generate tight bounding boxes for the tracking target and collect reliable samples for online training of the network, we propose an adaptive key point selection method to select the subset of reliable key points and drop the unreliable ones, based on the qualities of their corresponding response maps as well as the constraints from shape, scale and location. We demonstrate that the regressed corners can help naturally locate the tracking target with tight bounding boxes. The challenges of scale variation, shape deformation and occlusion can be handled explicitly. The commonly used time-consuming image pyramid based multi-scale testing method can also be discarded. Extensive experiments on OTB2013, OTB2015, UAV123, LaSOT, VOT2016 and VOT2018 datasets are conducted to report new state-of-the-art performances and demonstrate the effectiveness of CAT.

中文翻译：

---

CAT：具有深度回归网络的角点辅助跟踪


视觉媒体中的单个对象跟踪是一项重要但具有挑战性的任务。各种挑战，特别是目标尺度变化、形状变形和遮挡，会对跟踪器的性能产生很大影响。目前基于深度回归的跟踪器仅密切关注跟踪目标中心关键点的回归，同时采用基于图像金字塔的多尺度测试方法来处理尺度估计。这样的程序无法妥善应对这三个挑战。我们借助跟踪目标的四个边界框角的辅助回归，以原则性的方式解决这些挑战。在这项工作中，我们提出了具有深度回归网络的新型角点辅助跟踪器（Corner Aided Tracker），缩写为 CAT。与基于 RPN 的跟踪器不同，在 CAT 中，跟踪目标的边界框的四个角点以及中心关键点是通过五个相应的响应图同时获得的。此外，为了稳健、准确地为跟踪目标生成紧密的边界框并收集可靠的样本用于网络的在线训练，我们提出了一种自适应关键点选择方法，基于其相应响应图的质量以及形状、比例和位置的约束。我们证明，回归角可以帮助自然地定位具有紧密边界框的跟踪目标。可以明确地处理尺度变化、形状变形和遮挡的挑战。常用的耗时的基于图像金字塔的多尺度测试方法也可以被丢弃。 在 OTB2013、OTB2015、UAV123、LaSOT、VOT2016 和 VOT2018 数据集上进行了大量实验，以报告新的最先进性能并证明 CAT 的有效性。