  Visual object tracking aims to precisely estimate the bounding box for the
given target, which is a challenging problem due to factors such as deformation
and occlusion. Many recent trackers adopt the multiple-stage tracking strategy
to improve the quality of bounding box estimation. These methods first coarsely
locate the target and then refine the initial prediction in the following
stages. However, existing approaches still suffer from limited precision, and
the coupling of different stages severely restricts the method's
transferability. This work proposes a novel, flexible, and accurate refinement
module called Alpha-Refine (AR), which can significantly improve the base
trackers' box estimation quality. By exploring a series of design options, we
conclude that the key to successful refinement is extracting and maintaining
detailed spatial information as much as possible. Following this principle,
Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an
auxiliary mask head as the core components. Comprehensive experiments on
TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers
show that our approach significantly improves the base trackers' performance
with little extra latency. The proposed Alpha-Refine method leads to a series
of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp)
and the ARDiMP50 (ARstrengthened DiMP50) achieve good efficiency-precision
trade-off, while the ARDiMPsuper (AR strengthened DiMP-super) achieves very
competitive performance at a real-time speed. Code and pretrained models are
available at https://github.com/MasterBin-IIAU/AlphaRefine.
