For visual tracking, an ideal filter learned by the correlation filter (CF)
method should take both discrimination and reliability information. However,
existing attempts usually focus on the former one while pay less attention to
reliability learning. This may make the learned filter be dominated by the
unexpected salient regions on the feature map, thereby resulting in model
degradation. To address this issue, we propose a novel CF-based optimization
problem to jointly model the discrimination and reliability information. First,
we treat the filter as the element-wise product of a base filter and a
reliability term. The base filter is aimed to learn the discrimination
information between the target and backgrounds, and the reliability term
encourages the final filter to focus on more reliable regions. Second, we
introduce a local response consistency regular term to emphasize equal
contributions of different regions and avoid the tracker being dominated by
unreliable regions. The proposed optimization problem can be solved using the
alternating direction method and speeded up in the Fourier domain. We conduct
extensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets to
evaluate the proposed tracker. Experimental results show that our tracker
performs favorably against other state-of-the-art trackers.

本文提出了一种新的基于相关滤波器的优化问题，联合建模区分度和可靠性信息用于视觉跟踪，其中包括滤波器的基本成分和可靠性项，以及局部相应一致性正则项以强调不同区域的平等贡献。实验结果表明，我们的跟踪器表现优于其他同类跟踪器。