关键词: 长时跟踪, 孪生网络, 模板更新, 重检测

Abstract: In order to solve the problem of frequent disappearing and reappearing of targets due to occlusion and out of view in long-term target tracking scenes, a long-term target  tracking  algorithm based on update and redetection (LTUSiam) is designed. Firstly, based on the basic tracker Siamese region proposed network(SiamRPN), a three-level cascade gated cycle unit is introduced to judge the target state and choose the right time to update the template information adaptively. Secondly, a redetection algorithm based on template matching is proposed. The candidate region extraction module is used to relocate the target position and size, and the evaluation score sequence is used to judge the target loss to determine the tracking state of the next frame. Experiments show that the success rate and precision of LTUSiam on LaSOT dataset reach 0.566 and 0.556 respectively, and the F1-score of LTUSiam on VOT2018_LT dataset is 0.644, which has better robustness in dealing with target loss recurrence problem, and effectively improves the performance of long-term tracking.

Key words: long-term tracking, Siamese network, template update, redetection