RTrack: Accelerating Convergence for Visual Object Tracking via
  Pseudo-Boxes Exploration
Guotian Zeng Bi Zeng Hong Zhang Jianqi Liu Qingmao Wei
Single object tracking (SOT) heavily relies on the representation of the
target object as a bounding box. However, due to the potential deformation and
rotation experienced by the tracked targets, the genuine bounding box fails to
capture the appearance information explicitly and introduces cluttered
background. This paper proposes RTrack, a novel object representation baseline
tracker that utilizes a set of sample points to get a pseudo bounding box.
RTrack automatically arranges these points to define the spatial extents and
highlight local areas. Building upon the baseline, we conducted an in-depth
exploration of the training potential and introduced a one-to-many leading
assignment strategy. It is worth noting that our approach achieves competitive
performance to the state-of-the-art trackers on the GOT-10k dataset while
reducing training time to just 10% of the previous state-of-the-art (SOTA)
trackers' training costs. The substantial reduction in training costs brings
single-object tracking (SOT) closer to the object detection (OD) task.
Extensive experiments demonstrate that our proposed RTrack achieves SOTA
results with faster convergence.