A Long-Term Unmanned Aerial Vehicle Tracking Algorithm Using Local-Global Region Redetection Mechanism

被引：0
作者：
Huang H. [1 ,2 ]
Ma H. [1 ,2 ]
Liu G. [1 ]
Wang H. [2 ]
Gao T. [3 ]
Zhang K. [1 ,2 ]
机构：
[1] Xi'an Key Laboratory of Intelligent Expressway Information Fusion and Control, Chang'an University, Xi'an
[2] School of Electronic and Control Engineering, Chang'an University, Xi'an
[3] Institute of Data Science and Artificial Intelligence, Chang'an University, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2024年 / 58卷 / 06期
关键词：
correlation filter; fast scale filtering; heavy detector; high confidence; long-term tracking; unmanned aerial vehicle;
D O I：
10.7652/xjtuxb202406001
中图分类号：
学科分类号：
摘要：
In order to solve the problem that the basic tracker is prone to tracking failure in long-term tracking scenarios such as occlusion and out of view, a long-term tracking algorithm for unmanned aerial vehicle(UAV)based on local-global region redetection is proposed. The basic filter is designed, and the high-confidence samples are combined with it, and the adaptive spatio-temporal regularization is integrated to solve the filter degradation problem and improve the robustness of the model and its performance in complex scenarios. The filter update strategy is optimized, and the adaptive update is performed by evaluating the tracking results. A fast scale filter is designed to solve the problem of scale change in the tracking process. A local-global region redetection mechanism is designed. When the tracking fails, the re-detector is started to recover the tracking target and the local region re-detection is completed first. If the tracking recovery fails, the global region re-detector is used to continue to recover the target tracking state. The experimental results show that the precision and accuracy of the proposed algorithm on the UAV20L data set can reach 0.724 and 0.621 respectively, representing improvement of 25.9% and 20.6% respectively compared with the STRCF algorithm. Compared with the similar mainstream algorithms, the tracking effect of the algorithm is improved, which proves its effectiveness. © 2024 Xi'an Jiaotong University. All rights reserved.
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页码：1 / 13
页数：12
相关论文
共 25 条
[1]  
HUANG He, LI Wenlong, WU Kun, Et al., 3D UAV low altitude penetration optimization based on ALCE-SSA, Journal of Nanjing University(Natural Science), 58, 3, pp. 448-459, (2022)
[2]  
HAN Ruize, FENG Wei, GUO Qing, Et al., Single object tracking research: a survey, Chinese Journal of Computers, 45, 9, pp. 1877-1907, (2022)
[3]  
(2022)
[4]  
YAN Chao, TU Lianghui, WANG Yuhao, Et al., Application of unmanned aerial vehicle in civil field in China, Flight Dynamics, 40, 3, pp. 1-6, (2022)
[5]  
BOLME D S, BEVERIDGE J R, DRAPER B A, Et al., Visual object tracking using adaptive correlation filters, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544-2550, (2010)
[6]  
HENRIQUES J F, CASEIRO R, MARTINS P, Et al., Exploiting the circulant structure of tracking-by-detection with kernels, Computer Vision-ECCV 2012, pp. 702-715, (2012)
[7]  
HENRIQUES J F, CASEIRO R, MARTINS P, Et al., High-speed tracking with kernelized correlation filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 3, pp. 583-596, (2015)
[8]  
DANELLJAN M, KHAN F S, FELSBERG M, Et al., Adaptive color attributes for real-time visual tracking, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090-1097, (2014)
[9]  
DANELLJAN M, HAGER G, KHAN F, Et al., Accurate scale estimation for robust visual tracking, British Machine Vision Conference 2014, pp. 1-11, (2014)
[10]  
DANELLJAN M, HAGER G, KHAN F S, Et al., Discriminative scale space tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 8, pp. 1561-1575, (2017)
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