SiamX: An Efficient Long-term Tracker Using Cross-level Feature Correlation and Adaptive Tracking Scheme

Huajian Huang, Sai-Kit Yeung

Abstract: Siamese network based trackers have achieved significant progress in visual object tracking. For the sake of speed, they mainly rely on offline training to learn a mono-level feature correlation between a target template and a search region. During the tracking period, they use a fixed strategy to infer target positions over sequences regardless of target states. However, such approaches are vulnerable in case of long-term challenges e.g. large variance, presence of distractors, fast motion, or target disappearing and the like. In this paper, we propose a new tracking framework, referred to as SiamX, by exploiting cross-level Siamese features to learn robust correlations between the target template and search regions, and also adaptive inference strategies to prevent tracking loss and realize fast target re-localization. Extensive experiments on four benchmarks including VOT-2019, LaSOT, GOT-10k, and TrackingNet show our method significantly enhances the tracker's ability to resist variance and interference, and achieve state-of-the-art results at around 50 FPS.