While remarkable progress has been made in robust visual tracking, accurate
target state estimation still remains a highly challenging problem. In this
paper, we argue that this issue is closely related to the prevalent bounding
box representation, which provides only a coarse spatial extent of object. Thus
an effcient visual tracking framework is proposed to accurately estimate the
target state with a finer representation as a set of representative points. The
point set is trained to indicate the semantically and geometrically significant
positions of target region, enabling more fine-grained localization and
modeling of object appearance. We further propose a multi-level aggregation
strategy to obtain detailed structure information by fusing hierarchical
convolution layers. Extensive experiments on several challenging benchmarks
including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method
achieves new state-of-the-art performance while running at over 20 FPS.

提出了一种基于代表点表示的有效视觉跟踪框架，结合多层级聚合策略获取细致的结构信息，实现了对目标状态的精确估计，并在多个基准测试上取得了新的最佳性能。