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Noise-Aware Framework for Robust Visual Tracking
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2020-06-10 , DOI: 10.1109/tcyb.2020.2996245
Shengjie Li 1, 2 , Shuai Zhao 1, 2 , Bo Cheng 1, 2 , Junliang Chen 1
Affiliation  

Both siamese network and correlation filter (CF)-based trackers have exhibited superior performance by formulating tracking as a similarity measure problem, where a similarity map is learned by the correlation between a target template and a region of interest (ROI) with a cosine window. Nevertheless, this window function is usually fixed for various targets and not changed, undergoing significant noise variations during tracking, which easily makes model drift. In this article, we focus on the study of a noise-aware (NA) framework for robust visual tracking. To this end, the impact of various window functions is first investigated in visual tracking. We identify that the low signal-to-noise ratio (SNR) of windowed ROIs makes the above trackers degenerate. At the prediction phase, a novel NA window customized for visual tracking is introduced to improve the SNR of windowed ROIs by adaptively suppressing the variable noise according to the observation of similarity maps. In addition, to further optimize the SNR of windowed pyramid ROIs for scale estimation, we propose to use the particle filter to dynamically sample several windowed ROIs with more favorable signals in temporal domains instead of this pyramid ROIs extracted in spatial domains. Extensive experiments on the popular OTB-2013, OTB-50, OTB-2015, VOT2017, TC128, UAV123, UAV123@10fps, UAV20L, and LaSOT datasets show that our NA framework can be extended to many siamese and CF trackers and our variants obtain superior performance than baseline trackers with a modest impact on efficiency.

中文翻译:

用于鲁棒视觉跟踪的噪声感知框架

siamese network 和基于相关滤波器 (CF) 的跟踪器都通过将跟踪公式化为相似性度量问题展示了卓越的性能,其中通过目标模板和具有余弦窗口的感兴趣区域 (ROI) 之间的相关性来学习相似性图. 然而,这个窗函数对于各种目标通常是固定不变的,在跟踪过程中噪声变化很大,容易使模型漂移。在本文中,我们专注于研究用于鲁棒视觉跟踪的噪声感知 (NA) 框架。为此,首先在视觉跟踪中研究了各种窗口函数的影响。我们发现窗口化 ROI 的低信噪比 (SNR) 使上述跟踪器退化。在预测阶段,引入了一种为视觉跟踪定制的​​新型 NA 窗口,通过根据相似度图的观察自适应地抑制可变噪声来提高窗口 ROI 的 SNR。此外,为了进一步优化窗口金字塔 ROI 的 SNR 以进行尺度估计,我们建议使用粒子滤波器在时间域中动态采样几个具有更有利信号的窗口 ROI,而不是在空间域中提取金字塔 ROI。在流行的 OTB-2013、OTB-50、OTB-2015、VOT2017、TC128、UAV123、UAV123@10fps、UAV20L 和 LaSOT 数据集上进行的大量实验表明,我们的 NA 框架可以扩展到许多连体和 CF 跟踪器,并且我们的变体获得性能优于基线跟踪器,但对效率的影响不大。
更新日期:2020-06-10
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