IoU-guided Siamese network with high-confidence template fusion for visual tracking

Abstract

Existing IoU-guided trackers use IoU score to weight the classification score only in testing phase, this model mismatch between training and testing phases leads to poor tracking performance especially when facing background distractors. In this paper, we propose an IoU-guided Siamese network with High-confidence template fusion (SiamIH) for visual tracking. An IoU-guided distractor suppression network is proposed, which uses IoU information to guide classification in training phase and testing phase, and makes the tracking model to suppress background distractors. To cope with appearance variations, we design a high-confidence template fusion network that fuses APCE-based high-confidence template and the initial template to generate more reliable template. Experimental results on OTB2013, OTB2015, UAV123, LaSOT, and GOT10k demonstrate that the proposed SiamIH achieves state-of-the-art tracking performance.