  Despite the great success of Siamese-based trackers, their performance under
complicated scenarios is still not satisfying, especially when there are
distractors. To this end, we propose a novel Siamese relation network, which
introduces two efficient modules, i.e. Relation Detector (RD) and Refinement
Module (RM). RD performs in a meta-learning way to obtain a learning ability to
filter the distractors from the background while RM aims to effectively
integrate the proposed RD into the Siamese framework to generate accurate
tracking result. Moreover, to further improve the discriminability and
robustness of the tracker, we introduce a contrastive training strategy that
attempts not only to learn matching the same target but also to learn how to
distinguish the different objects. Therefore, our tracker can achieve accurate
tracking results when facing background clutters, fast motion, and occlusion.
Experimental results on five popular benchmarks, including VOT2018, VOT2019,
OTB100, LaSOT, and UAV123, show that the proposed method is effective and can
achieve state-of-the-art results. The code will be available at
https://github.com/hqucv/siamrn

Computer Vision and Pattern Recognition