Anti-UAV Target Tracking with Motion Association Integration

While the rapid development and widespread application of drone technology have brought about significant advancements, they have also introduced security challenges, making anti-UAV technology a key research focus. However, existing methods still face severe challenges when dealing with UAV tracking in complex scenarios. To address this, this paper proposes an integrated Motion-associated Detection and Tracking Collaboration (MDTC) system for anti-UAV applications. To better handle the perception of target existence states, we designed a motion association module that dynamically senses the presence of targets and responds quickly to target disappearance. Simultaneously, to address the issue of feature degradation in small targets, we optimized the detection branch to enhance robust perception of multi-scale targets. Additionally, the proposed verification matching mechanism can infer the integrity and reliability of targets in occluded scenarios, ensuring stable tracking. Compared to existing methods, our approach achieves superior performance across three benchmark datasets. On Anti-UAV600, it attains IoU, ACC, and SR scores of 0.525, 0.427, and 0.641, respectively—surpassing the second-best method, GlobalTrack, by 6.2%, 6.4%, and 5.9%. These gains highlight the method’s strengths in prompt target response, scale adaptability, and occlusion awareness, underscoring its reliability and practicality for real-world deployment.