Despite the demonstrated success of numerous correlation filter (CF) based tracking approaches, their assumption of circulant structure of samples introduces significant redundancy to learn an effective classifier. In this paper, we develop a fast manifold regularized context-aware correlation tracking algorithm that mines the local manifold structure information of different types of samples. First, different from the traditional CF based tracking that only uses one base sample, we employ a set of contextual samples near to the base sample, and impose a manifold structure assumption on them. Afterwards, to take into account the manifold structure among these samples, we introduce a linear graph Laplacian regularized term into the objective of CF learning. Fortunately, the optimization can be efficiently solved in a closed form with fast Fourier transforms (FFTs), which contributes to a highly efficient implementation. Extensive evaluations on the OTB100 and VOT2016 datasets demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms in terms of accuracy and robustness. Especially, our tracker is able to run in real-time with 28 fps on a single CPU.

中文翻译：

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实时流形正则化上下文感知相关跟踪

尽管已经证明了许多基于相关滤波器（CF）的跟踪方法的成功，但是他们对样本循环结构的假设引入了显着的冗余，以学习有效的分类器。在本文中，我们开发了一种快速的流形规则化的上下文相关关联跟踪算法，该算法可挖掘不同类型样本的局部流形结构信息。首先，与仅使用一个基础样本的传统​​基于CF的跟踪不同，我们在基础样本附近采用了一组上下文样本，并对其施加了多种结构假设。然后，考虑到这些样本之间的流形结构，我们将线性图拉普拉斯正则化项引入CF学习的目标。幸好，可以使用快速傅里叶变换（FFT）以封闭的形式有效地解决优化问题，这有助于实现高效。对OTB100和VOT2016数据集的广泛评估表明，就准确性和鲁棒性而言，所提出的跟踪器在性能上优于几种最新算法。特别是，我们的跟踪器能够在单个CPU上以28 fps的速度实时运行。