Multi-hierarchical Independent Correlation Filters for Visual Tracking

Understand

For visual tracking, most of the traditional correlation filters (CF) based methods suffer from the bottleneck of feature redundancy and lack of motion information.

• In this paper, we design a novel tracking framework, called multi-hierarchical independent correlation filters (MHIT).
• The framework consists of motion estimation module, hierarchical features selection, independent CF online learning, and adaptive multi-branch CF fusion.
• Specifically, the motion estimation module is introduced to capture motion information, which effectively alleviates the object partial occlusion in the temporal video.