Long-term object tracking based on template update and redetection
In order to solve the problem of frequent disappearing and reappearing of targets due to oc-clusion 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 re-locate 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 prob-lem,and effectively improves the performance of long-term tracking.
万方数据
维普
