AwareTrack: Object awareness for visual tracking via templates interaction

Abstract

Current popular trackers, whether based on the Siamese network or Transformer, have focused their main work on relation modeling between the template and the search area, and on the design of the tracking head, neglecting the fundamental element of tracking, the template. Templates are often mixed with too much background information, which can interfere with the extraction of template features. To address the above issue, a template object-aware tracker (AwareTrack) is proposed. Through the information interaction between multiple templates, the attention of the templates can be truly focused on the object itself, and the background interference can be suppressed. To ensure that the foreground objects of the templates have the same appearance to the greatest extent, the concept of awareness templates is proposed, which consists of two close frames. In addition, an awareness templates sampling method based on similarity discrimination via Siamese network is also proposed, which adaptively determines the interval between two awareness templates, ensure the maximization of background differences in the awareness templates. Meanwhile, online updates to the awareness templates ensure that our tracker has access to the most recent features of the foreground object. Our AwareTrack achieves state-of-the-art performance on multiple benchmarks, particularly on the one-shot tracking benchmark GOT-10k, achieving the AO of 78.1%, which is a 4.4% improvement over OSTrack-384.