Hybrid Cascade Filter With Complementary Features for Visual Tracking

Abstract

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.