Searching Region-Free and Template-Free Siamese Network for Tracking Drones in TIR Videos

With the growing threat of unmanned aerial vehicle (UAV) intrusions, the topic of anti-UAV tracking has received widespread attention from the community. Traditional Siamese trackers struggle with small UAV targets and are plagued by model degradation issues. To mitigate this, we propose a novel searching region-free and template-free Siamese network (SiamSRT) to track UAV targets in thermal infrared (TIR) videos. The proposed tracker builds a two-stage Siamese architecture with the former providing detection of the first-frame ground truth by using a cross-correlated region proposal network (C-C RPN) and the latter providing detection of previous-frame predictions via a similarity-learning region convolutional neural network (S-L RCNN). In both stage, global proposals are acquired by region of interest (ROI) alignment operation to break the limitation of searching region. Then, a spatial location consistency function is introduced to suppress background thermal distractors and a temporal memory bank (TMB) is utilized to avoid template update degradation problem. Further, a single-category foreground detector (SCFD) is designed to independently predict the position of the UAV target. SCFD can re-initialize the tracker without the given target in the first frame, which can help to recover the tracking failures. Comprehensive experiments demonstrate that SiamSRT achieves the best performance compared to the most advanced algorithms in the anti-UAV tracking missions.