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:
this https URL

本文提出了一种简单的基于目标感知的 Siamese 图注意力网络解决视觉跟踪中的相似性匹配问题，通过建立完全二分图建立目标和搜索区域之间的部分对部分对应关系，并应用图注意力机制从模板特征传播目标信息到搜索特征，使用目标感知区域选择机制适配不同对象的大小和纵横比变化，实验证明该算法优于现有基于深度学习的最先进跟踪器。