  Accurate tracking is still a challenging task due to appearance variations,
pose and view changes, and geometric deformations of target in videos. Recent
anchor-free trackers provide an efficient regression mechanism but fail to
produce precise bounding box estimation. To address these issues, this paper
repurposes a Transformer-alike regression branch, termed as Target Transformed
Regression (TREG), for accurate anchor-free tracking. The core to our TREG is
to model pair-wise relation between elements in target template and search
region, and use the resulted target enhanced visual representation for accurate
bounding box regression. This target contextualized representation is able to
enhance the target relevant information to help precisely locate the box
boundaries, and deal with the object deformation to some extent due to its
local and dense matching mechanism. In addition, we devise a simple online
template update mechanism to select reliable templates, increasing the
robustness for appearance variations and geometric deformations of target in
time. Experimental results on visual tracking benchmarks including VOT2018,
VOT2019, OTB100, GOT10k, NFS, UAV123, LaSOT and TrackingNet demonstrate that
TREG obtains the state-of-the-art performance, achieving a success rate of
0.640 on LaSOT, while running at around 30 FPS. The code and models will be
made available at https://github.com/MCG-NJU/TREG.
