Different features describe different aspects of the object. Individually tailoring proper features for visual tracking is crucial to obtain high performance. In this letter, we propose a hybrid cascade filter to fuse handcrafted and deep features for exploiting their strengths. We complement the deep representation with handcrafted features to achieve better localization accuracy, as well as build a hybrid cascade structure using multiple observation models to achieve better robustness. Furthermore, a coarse-to-fine searching strategy is used for lowering the computational cost. Extensive experimental results on two benchmark datasets show that the proposed method performs favorably against the state-of-the-art trackers.

中文翻译：

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具有用于视觉跟踪的互补功能的混合级联滤波器

不同的特征描述了对象的不同方面。为视觉跟踪单独定制合适的特征对于获得高性能至关重要。在这封信中，我们提出了一种混合级联过滤器，以融合手工制作和深层特征，以发挥其优势。我们用手工制作的特征补充深度表示以实现更好的定位精度，并使用多个观察模型构建混合级联结构以实现更好的鲁棒性。此外，从粗到细的搜索策略用于降低计算成本。在两个基准数据集上的大量实验结果表明，所提出的方法在对抗最先进的跟踪器时表现良好。