论文标题
图形注意跟踪
Graph Attention Tracking
论文作者
论文摘要
基于暹罗网络的跟踪器将视觉跟踪任务作为相似性匹配问题。几乎所有流行的暹罗跟踪器都通过目标分支和搜索分支之间的卷积特征互相关来实现相似性学习。但是，由于需要预先固定目标特征区域的大小，因此这些互相关的基础方法要么遭受保留大量不利的背景信息或缺少大量前景信息的影响。此外，目标和搜索区域之间的全球匹配也在很大程度上忽略了目标结构和零件级别的信息。 在本文中，为了解决上述问题，我们提出了一个简单的目标感知的暹罗图表网络，用于通用对象跟踪。我们建议使用完整的两部分图建立目标与搜索区域之间的部分对应关系，并应用图形注意机制将目标信息从模板特征传播到搜索功能。此外，我们研究了一种目标感知区域选择机制，以适合不同物体的大小和宽高比变化，而不是使用预固定的区域裁剪来进行模板 - 特征区域的选择。对包括GOT-10K，UAV123，OTB-100和LASOT在内的具有挑战性的基准进行的实验表明，拟议中的Siamgat的表现优于许多最先进的跟踪器，并取得了领先的性能。代码可在以下网址找到：https：//git.io/siamgat
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
