A fast long-term visual tracking algorithm based on deep learning

Current deep learning-based visual tracking algorithms have difficulty tracking the target accurately in real-time in complex long-term monitoring environments including target size change, occlusion, and out-of-view. To solve this problem, a fast long-term visual tracking algorithm is proposed, which consists of a fast short-term tracking algorithm and a fast global re-detection module. First, as a short-term tracking algorithm, the attention module of second-order channel and region spatial fusion is added to the base algorithm SiamRPN. Then, in order to make the improved short-term tracking algorithm have a fast long-term tracking ability, the global re-detection module based on template matching proposed in this paper is added to the algorithm, which uses a lightweight network and fast similarity judgment method to speed up the re-detection rate. The proposed algorithm is tested on five datasets (OTB100, LaSOT, UAV20L, VOT2018-LT, and VOT2020-LT). With an average tracking speed of 104 frames per second, the experimental findings demonstrate the algorithm's outstanding long-term tracking performance. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.