Hybrid Cascade Filter With Complementary Features for Visual Tracking
Hong Bo Zhu, Yusheng Han, Yong Wang, Guanglin Yuan · IEEE Signal Processing Letters · 2020
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.