Jointly modeling association and motion cues for robust infrared UAV tracking

UAV tracking plays a crucial role in computer vision by enabling real-time monitoring UAVs, enhancing safety and operational capabilities while expanding the potential applications of drone technology. Off-the-shelf deep learning based trackers have not been able to effectively address challenges such as occlusion, complex motion, and background clutter for UAV objects in infrared modality. To overcome these limitations, we propose a novel tracker for UAV object tracking, named MAMC. To be specific, the proposed method first employs a data augmentation strategy to enhance the training dataset. We then introduce a candidate target association matching method to deal with the problem of interference caused by the presence of a large number of similar targets in the infrared pattern. Next, it leverages a motion estimation algorithm with window jitter compensation to address the tracking instability due to background clutter and occlusion. In addition, a simple yet effective object research and update strategy is used to address the complex motion and localization problem of UAV objects. Experimental results demonstrate that the proposed tracker achieves state-of-the-art performance on the Anti-UAV and LSOTB-TIR dataset.