Since Correlation Filter appeared in the field of video object tracking, it is very popular due to its excellent performance. The Correlation Filter-based tracking algorithms are very competitive in terms of accuracy and speed as well as robustness. However, there are still some fields for improvement in the Correlation Filter-based tracking algorithms. First, during the training of the classifier, the background information that can be utilized is very limited. Moreover, the introduction of the cosine window further reduces the background information. These reasons reduce the discriminating power of the classifier. This paper introduces more global background information on the basis of the DCF tracker to improve the discriminating ability of the classifier. Then, in some complex scenes, tracking loss is easy to occur. At this point, the tracker will be treated the background information as the object. To solve this problem, this paper introduces a novel re-detection component. Finally, the current Correlation Filter-based tracking algorithms use the linear interpolation model update method, which cannot adapt to the object changes in time. This paper proposes an adaptive model update strategy to improve the robustness of the tracker. The experimental results on multiple datasets can show that the tracking algorithm proposed in this paper is an excellent algorithm.

中文翻译：

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基于 DCF 算法的鲁棒视觉跟踪器

自相关滤波器出现在视频对象跟踪领域以来，因其优异的性能而广受欢迎。基于相关滤波器的跟踪算法在准确性和速度以及鲁棒性方面非常具有竞争力。然而，基于相关滤波器的跟踪算法仍有一些需要改进的地方。首先，在分类器的训练过程中，可以利用的背景信息非常有限。此外，余弦窗口的引入进一步减少了背景信息。这些原因降低了分类器的判别力。本文在DCF tracker的基础上引入更多的全局背景信息，以提高分类器的判别能力。那么，在一些复杂的场景中，很容易出现跟踪丢失。在此刻，跟踪器会将背景信息视为对象。为了解决这个问题，本文引入了一种新颖的重新检测组件。最后，当前基于相关滤波器的跟踪算法采用线性插值模型更新方法，不能及时适应对象的变化。本文提出了一种自适应模型更新策略来提高跟踪器的鲁棒性。在多个数据集上的实验结果表明，本文提出的跟踪算法是一种优秀的算法。本文提出了一种自适应模型更新策略来提高跟踪器的鲁棒性。在多个数据集上的实验结果表明，本文提出的跟踪算法是一种优秀的算法。本文提出了一种自适应模型更新策略来提高跟踪器的鲁棒性。在多个数据集上的实验结果表明，本文提出的跟踪算法是一种优秀的算法。