Research On Unmanned Aerial Vehicle Tracking Algorithm For Low-altitude Airspace

With the rapid development of the drone industry in recent years,how to effectively monitor and track Uumanned Aerial Vehicle（UAV）targets and safeguard the safety of lowaltitude airspace has become an urgent scientific problem in UAV regulation..this paper conducts research on the difficulties of UAV target tracking based on a general target tracking algorithm with deep learning,and the main research is as follows:（1）To face the state estimation challenge in visual object tracking,we propose an anchorfree tracking framework based on Siamese network.The proposed framework consists of template features guided Siamese subnetwork and keypoints detection subnetwork.We take simplified hourglass network as backbone in Siamese subnetwork to improve the tracking efficiency and the prediction of corners around target instead of the prediction of target in keypoints detection subnetwork.We experiment our approach on OTB-2015 and VOT-2016,and our approach acquire the best precision of 0.841 on OTB-2015 and runs at 39 FPS.（2）We propose a novel Siamese Attentional Cascade Keypoints Tracking Network named SiamACN for the problem that the UAV target scale changes and the more complex environment.A simplified hourglass network with global attention is considered the backbone as well as a cascade corner pooling is used for keypoints prediction around the target to improve the tracking efficiency.In addition,we employ attentional maps to cope with the problem of UAV target scale variation.Since there is no corresponding UAV dataset,to certificate the superiority of our framework,extensive tests are conducted on five tracking benchmarks.Our method achieves the leading performance.We also tested our method qualitatively on the UAV dataset in LaSOT and achieved excellent tracking results,and favorably runs at 32 FPS against other competing algorithms,which confirms its effectiveness in real-time applications.（3）We have also conducted some research on UAV multi-target tracking.For the problem that the UAV tracking environment is more complex and small size of UAV,we propose a UAV multi-target tracking method based on a bidirectional Gated Recurrent Unit（GRU）-attention mechanism.Motion information combined with detection information is used in tracking to achieve detection of weak targets,and bidirectional GRU is used in the matching phase to achieve accurate matching of targets.It effectively solves the problem of changing the number of tracked targets when performing UAV multi-target tracking.