SwinMR: A Mutual Refinement Enhanced SwinTrack Framework

The task of tracking weak targets in low-altitude UAV scenarios requires high robustness and generalization ability of the model. Against this backdrop, this paper proposes a novel annotation and training mechanism based on SwinTrack. To improve the model’s tracking ability for weak targets, this paper proposes a pseudo-label consistency screening and background enhancement annotation strategy. This strategy enlarges the target box proportionally before training to obtain more effective background information. Furthermore, pseudo-labels are screened using a hybrid gating system of geometric overlap and confidence consistency to reduce the negative transfer interference of noise generated in different domains on the model. Since the data feature distribution varies significantly in tracking tasks, this paper introduces a mutual-teaching pseudo-label iterative training method into the field of weak target tracking. This aims to continuously transfer the model from the source domain to the target domain during iteration, thereby improving the model’s generalization ability. Experiments have shown that, when faced with a completely new dataset of weak target tracking, the proposed method improves upon recent strong baselines in single-target tracking by 0.05 in both P@20 and NP-AUC, and by 0.04 in SUS, demonstrating the enhanced tracking performance and generalization ability of the proposed method in the field of weak target tracking.