HFANTrack: A Hierarchical Feature-Aware Network for Visual Tracking

Fengwei Gu, Ao Li, Tienan Liu, Tao Shi, Ning Zhao, Zhaojie Ju

Abstract: In the field of visual tracking, it is a significant challenge to accurately capture the dynamic changes of targets in complex scenes. To address this issue, this paper proposes a novel Hierarchical Feature-Aware Network (HFAN) to improve tracking performance. The design of HFAN mainly includes two pivotal components: Feature-Enhanced Unit (FEU) and Hierarchical Feature-Aware Unit (HFAU). FEU enhances the richness and discriminative power of target representations by reinforcing features from the templates and search regions. HFAU establishes comprehensive dependencies among multi-level features to capture the changing characteristics of targets across different spatial hierarchies. Finally, a Siamese tracker called HFANTrack is proposed to improve tracking accuracy and robustness in complex scenarios. Extensive experimental results show that our method achieves competitive tracking performance with a real-time speed of 49.3fps compared to state-of-the-art methods.