Learning Distortion-Aware Correlation Filters With Target Mask for UAV Tracking

Recently, due to the excellent computational efficiency and interpretability, discriminative correlation filter (DCF)-based tracking methods have received extensive attention in the field of unmanned aerial vehicles (UAVs). However, existing methods are usually susceptible to interference from significant appearance changes of the target object or background occlusion, which leads to tracking failure. To effectively address these issues, we propose a distortion-aware correlation filter with target mask (DACFTM) for UAV, which introduces a target regularization term to enhance the target perception ability of the tracking model. Specifically, we construct a target mask matrix based on the highest peak of the response map of the previous frame, thereby leveraging prior reliable localization confidence, and multiply it with the current feature map to obtain a regularization term containing only target information, effectively distinguishing the target from the background. In addition, to deal with tracking failure caused by large appearance changes, we propose a distortion-aware mechanism. When the quality of the response map corresponding to the filter is higher than a set threshold, we consider the filter is reliable and adopt the filter fusion strategy; otherwise, the saved high-quality filter is selected for the tracking in the next frame. Finally, we comprehensively evaluate the performance of DACFTM on three mainstream UAV benchmark datasets, and experimental results demonstrate that the DACFTM achieves impressive tracking performance.