基 于 高 分 辨 率 孪 生 网 络 的 无 人 机 目 标 跟 踪 算 法

Unmanned Aerial Vehicle (UAV) target tracking tasks are often suffer from small target size, large scale variance and frequent viewpoint change. To address these issues, in this paper, an UAV target tracking algorithm based on high-resolution siamese network is proposed. Firstly, a high-resolution network is improved as the feature extraction backbone network (Lite-high resolution network, L-HRNet), and a dynamic multi-template strategy is used to mine the inter-frame information of the video. Secondly, a multi-frame feature fusion module is constructed to obtain fusion features that are beneficial to target localization. Finally, an anchor-free strategy is selected to locate the target position and obtain accurate tracking results. The experimental results show that the success rate and accuracy of the proposed algorithm are 66.0% and 84.7% on DTB70 dataset, 65.7% and 84.3% on UAV123 dataset respectively, which improves the target tracking performance effectively.