  Correlation acts as a critical role in the tracking field, especially in
recent popular Siamese-based trackers. The correlation operation is a simple
fusion manner to consider the similarity between the template and the search
region. However, the correlation operation itself is a local linear matching
process, leading to lose semantic information and fall into local optimum
easily, which may be the bottleneck of designing high-accuracy tracking
algorithms. Is there any better feature fusion method than correlation? To
address this issue, inspired by Transformer, this work presents a novel
attention-based feature fusion network, which effectively combines the template
and search region features solely using attention. Specifically, the proposed
method includes an ego-context augment module based on self-attention and a
cross-feature augment module based on cross-attention. Finally, we present a
Transformer tracking (named TransT) method based on the Siamese-like feature
extraction backbone, the designed attention-based fusion mechanism, and the
classification and regression head. Experiments show that our TransT achieves
very promising results on six challenging datasets, especially on large-scale
LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively
50 fps on GPU. Code and models are available at
https://github.com/chenxin-dlut/TransT.
