ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual
  Tracking
Yutong Kou Jin Gao Bing Li Gang Wang Weiming Hu Yizheng Wang Liang Li
Recently, the transformer has enabled the speed-oriented trackers to approach
state-of-the-art (SOTA) performance with high-speed thanks to the smaller input
size or the lighter feature extraction backbone, though they still
substantially lag behind their corresponding performance-oriented versions. In
this paper, we demonstrate that it is possible to narrow or even close this gap
while achieving high tracking speed based on the smaller input size. To this
end, we non-uniformly resize the cropped image to have a smaller input size
while the resolution of the area where the target is more likely to appear is
higher and vice versa. This enables us to solve the dilemma of attending to a
larger visual field while retaining more raw information for the target despite
a smaller input size. Our formulation for the non-uniform resizing can be
efficiently solved through quadratic programming (QP) and naturally integrated
into most of the crop-based local trackers. Comprehensive experiments on five
challenging datasets based on two kinds of transformer trackers, \ie, OSTrack
and TransT, demonstrate consistent improvements over them. In particular,
applying our method to the speed-oriented version of OSTrack even outperforms
its performance-oriented counterpart by 0.6% AUC on TNL2K, while running 50%
faster and saving over 55% MACs. Codes and models are available at
https://github.com/Kou-99/ZoomTrack.