Fast Visual Tracking With Siamese Oriented Region Proposal Network
Hong Bo Zhu, Mogen Xue, Yong Wang, Guanglin Yuan, Xin Li · IEEE Signal Processing Letters · 2022
Current oriented visual tracking depends on segmentation-driven framework brings about expensive computation cost, which becomes the bottleneck in the practical application. This paper proposes a simple and effective Siamese oriented Region Proposal Network (Siamese-ORPN) for visual tracking. Specifically, we propose to use oriented RPN on the similarity feature maps to directly generate high-quality oriented proposals in a nearly cost-free manner. Moreover, a top-down feature fusion network is proposed as the backbone for feature extraction and feature fusion, which can achieve substantial gains from the diversity of visual-semantic hierarchies. The Siamese-ORPN runs at 85 fps while achieving leading performance on the benchmark datasets including VOT2018 (44.6% EAO) and VOT2019 (39.6% EAO).