Transactions of Nanjing University of Aeronautics & Astronautics
SiamADN: Siamese Attentional Dense Network for UAV Object Tracking
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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.
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Project Supported:
This study was supported by the Zhejiang Key Laboratory of General Aviation Operation Technology （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 Innovation Talent Project of Shenyang （No.RC190030）.
Clc Number:
TN925
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