In this paper, we propose a robust visual tracking method based on mutual kernelized correlation filters with elastic net constraint. First, two correlation filters are trained in a general framework jointly in a closed form, which are interrelated and interacted on each other. Second, elastic net constraint is imposed on each discriminative filter, which is able to filter some interfering features. Third, scale estimation and target re-detection scheme are adopted in our framework, which can deal with scale variation and tracking failure effectively. Extensive experiments on some challenging tracking benchmarks demonstrate that our proposed method is able to obtain a competitive tracking performance against other state-of-the-art algorithms.

中文翻译：

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具有弹性网约束的相互核化相关滤波器，用于视觉跟踪

在本文中，我们提出了一种基于具有弹性网约束的互核相关滤波器的鲁棒视觉跟踪方法。首先，在一个通用框架中以封闭形式联合训练两个相关过滤器，它们相互关联并相互影响。其次，对每个判别滤波器施加弹性净约束，该约束能够过滤某些干扰特征。第三，在我们的框架中采用规模估计和目标重新检测方案，可以有效地处理规模变化和跟踪故障。在一些具有挑战性的跟踪基准上进行的大量实验表明，我们提出的方法能够获得与其他最新算法相比具有竞争力的跟踪性能。