Siamese Graph Attention Networks for robust visual object tracking
Junjie Lu, Shengyang Li, Weilong Guo, Manqi Zhao, Jian Yang, Yunfei Liu, Zhuang Zhou
Abstract
Siamese-based trackers usually convert the object tracking task into a similarity matching problem between the target template and the search region. Since fixed or manually updated templates are not robust when tracking moving objects with dramatically changing appearance, this paper proposes an improved siamese graph attention network with adaptive template update called SiamGT. By establishing spatiotemporal and context dependencies between historical images and search regions, a frame selection mechanism is added to improve the richness of information. In addition, a graph attention network with residual connections is used in the template update mechanism which enables the propagation and aggregation of information to generate robust templates. Extensive experimental results on challenging benchmarks such as UAV123, OTB100, and VOT2019 demonstrate that the proposed SiamGT has achieved state-of-the-art performance in visual object tracking.