Handling Occlusion in UAV Visual Tracking With Query-Guided Redetection

Aerial tracking has recently shown significant potential in vision-based measurement. Despite significant improvements, accurate occlusion tracking remains a difficult challenge. Existing training enhancement or global search struggles to handle occlusion in aerial views due to similar distractors and background interferences. To handle occlusion in aerial tracking, we propose a query-guided redetection tracker (QRDT) based on a Siamese neural network, improving the redetection discrimination from three stages. First, the query update (QU) branch is introduced to keep the target appearance dynamically updated via interframe information transfer. Next, we propose the cross-fusion layer (CFL), which models the semantic correlation between the search feature and the updated query feature, to draw attention to the occluded target. Finally, to address tracking failure and distraction from similar targets, the trajectory during full occlusion (FO) is reliably predicted by the Kalman filter. Our tracker achieves leading tracking performance on several benchmarks, with an average speed of 48.9 frames/s. The code and models are available at
https://github.com/xyl-507/QRDT
.