Dual-template Siamese Network with Cross-Correlation Fusion for Tracking

Siamese network based trackers treat tracking as a maximum matching between the detection region and the template, in which the template is either fixed or updated. The template-fixed trackers degrade accuracy since the appearance variations of the object in the subsequent frames are lacked; while the template-updated trackers depress robustness once the tracking drift occurs. In this paper, we apply a dual-template tracking strategy in the Siamese region proposal network to compensate for the separate merits of fixed template and mutative template. As for the dual-template, the one template is fixed and the other template is constantly updated. Meanwhile, we integrate a learnable convolutional neural network to fuse two Cross-Correlation responses generated from two template tracking branches. Based on Siamese network, we try to utilize the complementary fusion of different template responses to promote tracking performances. Extensive experiments on five tracking benchmarks of OTB100, VOT2016, VOT2018, GOT-10k and UAV123 demonstrate that our approach achieves the state-of-the-art tracking performances.