Siamese Dual Path Aggregation Network for Object Tracking

Siamese trackers have attracted great attention on visual object tracking due to their real-time speed and high accuracy. In this paper, we propose a dual path aggregation network (SiamDPAN) for high-performance tracking. First, we build a multi-level similarity maps aggregation (MSA) structure, which predicts and fuses the similarity maps from multi-level features. Second, we propose a mask path aggregation module (MPA) for better capturing the appearance changes of objects by propagating maps in low-layers. We conduct sufficient ablation studies to demonstrate the effectiveness of our proposed tracker. We only train our network with two datasets, achieving 0.436 EAO and 0.351 EAO on VOT2016 and VOT2018.