Transformers have been successfully applied to the visual tracking task and
significantly promote tracking performance. The self-attention mechanism
designed to model long-range dependencies is the key to the success of
Transformers. However, self-attention lacks focusing on the most relevant
information in the search regions, making it easy to be distracted by
background. In this paper, we relieve this issue with a sparse attention
mechanism by focusing the most relevant information in the search regions,
which enables a much accurate tracking. Furthermore, we introduce a double-head
predictor to boost the accuracy of foreground-background classification and
regression of target bounding boxes, which further improve the tracking
performance. Extensive experiments show that, without bells and whistles, our
method significantly outperforms the state-of-the-art approaches on LaSOT,
GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the
training time of our method is reduced by 75% compared to that of TransT. The
source code and models are available at this https URL

通过引入稀疏注意力机制和双头预测器，成功地解决了基于 Transformer 模型的视觉跟踪任务中自注意力机制关注背景信息而影响性能的问题，并在 LaSOT、GOT-10k、TrackingNet 和 UAV123 等数据集中表现出显著的跟踪性能。相比 TransT，我们的方法将训练时间缩短了 75％。