HFFTrack: Transformer tracking via hybrid frequency features

Abstract

Numerous Transformer-based trackers have emerged due to the powerful global modeling capabilities of the Transformer. Nevertheless, the Transformer is a low-pass filter with insufficient capacity to extract high-frequency features of the target and these features are essential for target location in tracking tasks. To address this issue, this paper proposes a tracking algorithm that utilizes hybrid frequency features, which explores how to improve the performance of the tracker by fusing target multi-frequency features. Specifically, a novel feature extraction network is designed that uses CNN and Transformer to learn the multi-frequency features of the target in stages, taking advantage of both structures and balancing high- and low-frequency information. Secondly, a dual-branch encoder is designed to allow the tracker to capture global information while learning the local features of the target through another branch. Finally, a multi-frequency features fusion network is designed that uses wavelet transform and convolution to fuse high-frequency and low-frequency features. Extensive experimental results demonstrate that our tracker achieves superior tracking performance on six challenging benchmark datasets (i.e., LaSOT, TrackingNet, GOT-10k, TNL2K, UAV123, and OTB100).