Siamese hierarchical feature fusion transformer for efficient tracking.

ABSTRACT: Object tracking is a fundamental task in computer vision. Recent years, most of the tracking algorithms are based on deep networks. Trackers with deeper backbones are computationally expensive and can hardly meet the real-time requirements on edge platforms. Lightweight networks are widely used to tackle this issue, but the features extracted by a lightweight backbone are inadequate for discriminating the object from the background in complex scenarios, especially for small objects tracking task. In this paper, we adopted a lightweight backbone and extracted features from multiple levels. A hierarchical feature fusion transformer (HFFT) was designed to mine the interdependencies of multi-level features in a novel model-SiamHFFT. Therefore, our tracker can exploit comprehensive feature repr

SUBMITTER: Dai J 

PROVIDER: S-EPMC9752039 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature