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.

本文介绍了一种名为 RTrack 的新型目标表示基准追踪器，该追踪器利用一组样本点来获取伪边界框，并自动排列这些点以定义空间范围和突出显示局部区域。我们还对训练潜力进行了深入研究，并引入了一对多的前导分配策略，该方法在 GOT-10k 数据集上实现了与最先进的追踪器竞争性性能，并将训练时间减少到了先前性能最佳追踪器的 10%。大量实验证明，我们提出的 RTrack 在更快的收敛速度下取得了最先进的结果。