Adaptive UAV target tracking algorithm based on residual learning

被引：0
|
作者：
Liu F. [1 ]
Sun Y. [1 ]
Wang H. [1 ]
Han X. [1 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
来源：
Liu, Fang (liufang@emails.bjut.edu.cn) | 1874年 / Beijing University of Aeronautics and Astronautics (BUAA)卷 / 46期
基金：
中国国家自然科学基金;
关键词：
Correlation filter; Dilated convolution; Residual learning; Scale adaptation; Target tracking; UAV;
D O I：
10.13700/j.bh.1001-5965.2019.0551
中图分类号：
学科分类号：
摘要：
UAVs have been widely used in military and civilian applications, and target tracking technology is one of the key technologies for UAV applications. Aimed at the problem that the target is prone to scale change and occlusion during the target tracking process of the UAV, an adaptive UAV video target tracking algorithm based on residual learning is proposed. Firstly, by combining the advantages of residual learning and dilated convolution, a depth network is constructed to extract target features and overcome the problem of network degradation. Secondly, the extracted feature information is input into the kernel correlation filtering algorithm, and a positioning filter is constructed to determine the central position of the target. Finally, adaptive segmentation is performed according to the different appearance characteristics of the target and the scaling coefficient of the target scale is calculated. The simulation results show that the algorithm can effectively deal with the influence of scale change and occlusion on tracking performance, and has higher tracking success rate and accuracy than other comparison algorithms. © 2020, Editorial Board of JBUAA. All right reserved.
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页码：1874 / 1882
页数：8
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