Ge Shiming, Zhang Chunhui, Li Shikun, Zeng Dan, Tao Dacheng
IEEE Trans Neural Netw Learn Syst. 2021 Mar;32(3):1276-1288. doi: 10.1109/TNNLS.2020.2984256. Epub 2021 Mar 1.
Recent deep trackers have shown superior performance in visual tracking. In this article, we propose a cascaded correlation refinement approach to facilitate the robustness of deep tracking. The core idea is to address accurate target localization and reliable model update in a collaborative way. To this end, our approach cascades multiple stages of correlation refinement to progressively refine target localization. Thus, the localized object could be used to learn an accurate on-the-fly model for improving the reliability of model update. Meanwhile, we introduce an explicit measure to identify the tracking failure and then leverage a simple yet effective look-back scheme to adaptively incorporate the initial model and on-the-fly model to update the tracking model. As a result, the tracking model can be used to localize the target more accurately. Extensive experiments on OTB2013, OTB2015, VOT2016, VOT2018, UAV123, and GOT-10k demonstrate that the proposed tracker achieves the best robustness against the state of the arts.
最近的深度跟踪器在视觉跟踪方面表现出卓越的性能。在本文中，我们提出了一种级联相关细化方法，以增强深度跟踪的鲁棒性。核心思想是以协作的方式解决精确的目标定位和可靠的模型更新问题。为此，我们的方法级联多个相关细化阶段，以逐步细化目标定位。这样，定位的对象可用于学习一个精确的实时模型，以提高模型更新的可靠性。同时，我们引入一种明确的度量来识别跟踪失败，然后利用一种简单而有效的回溯方案，自适应地结合初始模型和实时模型来更新跟踪模型。结果，跟踪模型可用于更精确地定位目标。在OTB2013、OTB2015、VOT2016、VOT2018、UAV123和GOT-10k上进行的大量实验表明，所提出的跟踪器相对于现有技术实现了最佳的鲁棒性。
