  Siamese network based trackers formulate the visual tracking task as a
similarity matching problem. Almost all popular Siamese trackers realize the
similarity learning via convolutional feature cross-correlation between a
target branch and a search branch. However, since the size of target feature
region needs to be pre-fixed, these cross-correlation base methods suffer from
either reserving much adverse background information or missing a great deal of
foreground information. Moreover, the global matching between the target and
search region also largely neglects the target structure and part-level
information.
  In this paper, to solve the above issues, we propose a simple target-aware
Siamese graph attention network for general object tracking. We propose to
establish part-to-part correspondence between the target and the search region
with a complete bipartite graph, and apply the graph attention mechanism to
propagate target information from the template feature to the search feature.
Further, instead of using the pre-fixed region cropping for
template-feature-area selection, we investigate a target-aware area selection
mechanism to fit the size and aspect ratio variations of different objects.
Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and
LaSOT demonstrate that the proposed SiamGAT outperforms many state-of-the-art
trackers and achieves leading performance. Code is available at:
https://git.io/SiamGAT
