AHA-track: Aggregating hierarchical awareness features for single

Abstract

Single Object Tracking (SOT) plays a crucial role in various real-world applications but still faces significant challenges, including scale variations and background distractions. While Vision Transformers (ViTs) have demonstrated improvements in tracking performance, they are often hindered by high computational costs. To address these issues, this paper propose a lightweight single object tracking model by aggregating hierarchical awareness features (AHA-Track). The template information is aggregated by aggregate token awareness module, and the key points of template are highlighted to reduce background interference. In addition, the hierarchical deep feature aggregation module has a more comprehensive understanding of object at different resolutions. It ultimately helps to improve the accuracy and robustness of challenging tracking scenes. AHA-Track enhances both tracking accuracy and speed, while maintaining computational efficiency. Extensive experimental evaluations across several benchmark datasets demonstrate that AHA-Track outperforms existing state-of-the-art methods in terms of both tracking accuracy and efficiency. The codes and pretrained models are available at https://github.com/YangMinbobo/AHATrack.