  In recent years, target tracking has made great progress in accuracy. This
development is mainly attributed to powerful networks (such as transformers)
and additional modules (such as online update and refinement modules). However,
less attention has been paid to tracking speed. Most state-of-the-art trackers
are satisfied with the real-time speed on powerful GPUs. However, practical
applications necessitate higher requirements for tracking speed, especially
when edge platforms with limited resources are used. In this work, we present
an efficient tracking method via a hierarchical cross-attention transformer
named HCAT. Our model runs about 195 fps on GPU, 45 fps on CPU, and 55 fps on
the edge AI platform of NVidia Jetson AGX Xavier. Experiments show that our
HCAT achieves promising results on LaSOT, GOT-10k, TrackingNet, NFS, OTB100,
UAV123, and VOT2020. Code and models are available at
https://github.com/chenxin-dlut/HCAT.
