Existing regression based tracking methods built on correlation filter model or convolution model do not take both accuracy and robustness into account at the same time. In this paper, we propose a dual-regression framework comprising a discriminative fully convolutional module and a fine-grained correlation filter component for visual tracking. The convolutional module trained in a classification manner with hard negative mining ensures the discriminative ability of the proposed tracker, which facilitates the handling of several challenging problems, such as drastic deformation, distractors, and complicated backgrounds. The correlation filter component built on the shallow features with fine-grained features enables accurate localization. By fusing these two branches in a coarse-to-fine manner, the proposed dual-regression tracking framework achieves a robust and accurate tracking performance. Extensive experiments on the OTB2013, OTB2015, and VOT2015 datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

建立在相关滤波器模型或卷积模型上的基于回归的现有跟踪方法没有同时考虑准确性和鲁棒性。在本文中，我们提出了一个双回归框架，该框架包括一个判别式完全卷积模块和一个用于视觉跟踪的细粒度相关滤波器组件。以分类方式训练的卷积模块具有坚硬的负性挖掘功能，可确保所提出的跟踪器具有判别能力，从而有助于处理一些具有挑战性的问题，例如剧烈变形，干扰因素和复杂的背景。基于浅层特征和细粒度特征的相关滤波器组件可实现精确定位。通过从粗到细的方式融合这两个分支，所提出的双回归跟踪框架可实现强大而准确的跟踪性能。在OTB2013，OTB2015和VOT2015数据集上进行的大量实验表明，提出的算法与最新方法相比具有出色的性能。