Multi-hierarchical Independent Correlation Filters for Visual Tracking

TL;DR

This paper introduces a multi-hierarchical correlation filter framework for visual tracking that leverages motion estimation and multi-scale features to improve robustness and accuracy, outperforming state-of-the-art methods.

Contribution

The paper proposes a novel MHIT framework combining motion estimation, hierarchical feature selection, independent CF learning, and adaptive fusion for enhanced visual tracking.

Findings

Significantly improves tracking performance on OTB and VOT datasets.

Achieves 20.1% relative gain over top trackers on VOT2017.

Sets new state-of-the-art results on VOT2018.

Abstract

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. The multi-hierarchical deep features of CNN representing different semantic information can be fully excavated to track multi-scale objects. To better overcome the deep feature redundancy, each hierarchical features are independently fed into a single branch to implement the online learning of parameters. Finally, an adaptive weight scheme is integrated into the framework to fuse these independent multi-branch CFs for the better and more robust visual object tracking. Extensive experiments on OTB and VOT datasets show that the proposed MHIT tracker can significantly improve the tracking performance. Especially, it obtains a 20.1% relative performance gain compared to the top trackers on the VOT2017 challenge, and also achieves new state-of-the-art performance on the VOT2018 challenge.