Currently, the discriminative correlation filter-based trackers have achieved higher tracking accuracy. However, visual tracking still faces challenges in terms of heavy occlusion, scale variation and so on. In this study, the authors intend to solve heavy occlusion by introducing collaborative model into classifier-box. Firstly, they introduce complex colour features into correlation filter tracker to improve the effect of the tracker. Secondly, they introduce a multi-scale method into their tracker to ease the scale problem. Thirdly, in order to solve the heavy occlusion in the tracking process, they adopt the locally weighted distance and classifier-box. Their algorithm achieves distance precision rates of 81.7 and 77.4% on OTB2013 dataset and OTB2015 dataset, respectively. Their contribution focuses on solving heavy occlusion by using colour features, locally weighted distance and classifier-box. The experimental results on OTB2013 and OTB2015 datasets demonstrate their algorithm to perform better than state-of-the-art methods.

中文翻译：

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具有强大遮挡处理功能的协作模型跟踪

当前，基于区分相关滤波器的跟踪器已经实现了更高的跟踪精度。然而，视觉跟踪仍然在重度遮挡，尺度变化等方面面临挑战。在这项研究中，作者打算通过将协作模型引入分类器盒来解决重度遮挡问题。首先，他们将复杂的色彩特征引入到相关过滤器跟踪器中，以提高跟踪器的效果。其次，他们在跟踪器中引入了多尺度方法来缓解尺度问题。第三，为了解决跟踪过程中的重遮挡问题，他们采用局部加权距离和分类器盒。他们的算法在OTB2013数据集和OTB2015数据集上的距离精度分别达到81.7％和77.4％。他们的贡献集中在通过使用颜色特征，局部加权距离和分类器来解决重度遮挡。在OTB2013和OTB2015数据集上的实验结果表明，它们的算法性能优于最新方法。