南京航空航天大学学报（英文版）2021，Vol.38Issue(4)：587-596,10.
SiamADN:用于无人机目标跟踪的孪生注意密集网络
SiamADN:Siamese Attentional Dense Network for UAV Object Tracking
摘要
Abstract
Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision. However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions. We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking. First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer. Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions. The advance corner detection network is introduced to improve the following tracking process. Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT. The accuracy rate on UAV123 is 78.9％,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.关键词
无人机/目标跟踪/密集网络/角点检测/孪生网络Key words
unmanned aerial vehicle(UAV)/object tracking/dense network/corner detection/siamese network分类
信息技术与安全科学引用本文复制引用
王志,王尔申,黄煜峰,杨斯淇,徐嵩..SiamADN:用于无人机目标跟踪的孪生注意密集网络[J].南京航空航天大学学报（英文版）,2021,38(4):587-596,10.基金项目
This study was supported by the Zhe-jiang Key Laboratory of General Aviation Operation Tech-nology(No.JDGA2020-7),the National Natural Science Foundation of China(No.62173237),the Natural Science Foundation of Liaoning Province(No.2019-MS-251),the Talent Project of Revitalization Liaoning Province(No.XLYC1907022),the Key R&D Projects of Liaoning Province(No.2020JH2/10100045),and the High-Level Innova-tion Talent Project of Shenyang(No.RC190030). （No.JDGA2020-7）
