Correlation filtering based tracking model has received significant attention and achieved great success in terms of both tracking accuracy and computational complexity. However, due to the limitation of the loss function, current correlation filtering paradigm could not reliably respond to the abrupt appearance changes of the target object. This study focuses on improving the robustness of the correlation filter learning. An anisotropy of the filter response is observed and analyzed for the correlation filtering based tracking model, through which the overfitting issue of previous methods is alleviated. Three sparsity related loss functions are proposed to exploit the anisotropy, leading to three implementations of visual trackers, correspondingly resulting in improved overall tracking performance. A large number of experiments are conducted and these experimental results demonstrate that the proposed approach greatly improves the robustness of the learned correlation filter. The proposed trackers performs comparably against state-of-the-art tracking methods on four latest standard tracking benchmark datasets.

中文翻译：

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利用相关滤波器学习的各向异性进行视觉跟踪

基于相关滤波的跟踪模型在跟踪精度和计算复杂度方面受到了极大的关注并取得了巨大的成功。然而，由于损失函数的限制，当前的相关过滤范式不能可靠地响应目标对象的突然外观变化。本研究侧重于提高相关滤波器学习的鲁棒性。对于基于相关滤波的跟踪模型，观察并分析了滤波器响应的各向异性，从而缓解了先前方法的过拟合问题。提出了三个与稀疏相关的损失函数来利用各向异性，导致视觉跟踪器的三种实现，相应地提高了整体跟踪性能。进行了大量实验，这些实验结果表明，所提出的方法大大提高了学习相关滤波器的鲁棒性。所提出的跟踪器在四个最新的标准跟踪基准数据集上的性能与最先进的跟踪方法相当。