The discriminative correlation filter (DCF) method is widely used in target tracking due to its real-time performance. However, the computational efficiency of DCF results in boundary effect, which reduces the tracking accuracy in fast motion scene. Besides, background noise is always required to be carefully handled for they will cause trouble in scenes such as background clutter, occlusion, deformation etc. To address the two issues, this study proposes masked discriminative correlation filter, which uses mask to process DCF filter as well as target samples so as to suppress boundary effect and background noise. Experimental results on benchmark datasets show that the proposed tracker performs better than a series of benchmark trackers, and is superior to them in almost various scenes.

中文翻译：

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带有掩盖鉴别相关滤波器的目标跟踪器

判别相关滤波器（DCF）方法由于其实时性能而广泛用于目标跟踪。但是，DCF的计算效率导致边界效应，从而降低了快速运动场景中的跟踪精度。此外，由于背景噪声会在诸如背景杂波，遮挡，变形等场景中造成麻烦，因此始终需要谨慎处理。为解决这两个问题，本研究提出了一种掩盖判别相关滤波器，该算法使用掩膜对DCF滤波器进行处理。以及目标样本，以抑制边界效应和背景噪声。在基准数据集上的实验结果表明，所提出的跟踪器的性能优于一系列基准跟踪器，并且在几乎各种场景中均优于它们。