基于自适应模板更新的Transformer无人机目标跟踪算法

doi:10.7527/S1000-6893.2025.31687

Abstract

Abstract:

Unmanned Aerial Vehicles （UAVs） have been extensively deployed in both military and civilian applications， where target tracking plays a critical role. To address challenges such as target deformation， occlusion， scale variation， and complex environmental conditions during UAV target tracking， a adaptive template update-based Transformer algorithm for UAV target tracking is proposed. Specifically， a Transformer backbone network is constructed using an improved asymmetric attention mechanism to effectively extract image features and enhance the representation of target-related information. Furthermore， an adaptive template updating strategy based on an appearance variation coefficient is introduced. By dynamically computing this coefficient， the template is updated adaptively to improve the ability of network to cope with appearance changes of the target. Finally， the target position is determined by calculating the maximum confidence score from the response map of the search region. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of UAV target tracking and exhibits strong robustness.

Key words: machine vision, Unmanned Aerial Vehicle (UAV), target tracking, template update, deep network

CLC Number:

V279

Fang LIU, Chenyang LU, Yan LU, Xin WANG. Adaptive template update-based Transformer algorithm for UAV target tracking[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(16): 331687.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Ablation experiment

序号	改进非对称注意力机制	普通模板更新	自适应模板更新	UAV123		GOT-10K		速度/（frame·s^-1）
序号	改进非对称注意力机制	普通模板更新	自适应模板更新	AUC/%	P/%	AOR/%	$S R 0.5$ /%	速度/（frame·s^-1）
1	$×$	$×$	$×$	67.9	90.1	70.7	80.0	51
2	$√$	$×$	$×$	69.7	91.3	72.9	82.9	50
3	$√$	$√$	$×$	67.5	88.1	73.0	83.0	40
4	$√$	$×$	$√$	70.0	91.6	73.5	83.4	40

Table 1

Table 2

Fig.6

References 28

[1]	管皓，薛向阳，安志勇. 深度学习在视频目标跟踪中的应用进展与展望［J］. 自动化学报， 2016， 42（6）： 834-847.
	GUAN H， XUE X Y， AN Z Y. Advances on application of deep learning for video object tracking［J］. Acta Automatica Sinica， 2016， 42（6）： 834-847 （in Chinese）.
[2]	刘芳，杨安喆，吴志威. 基于自适应Siamese网络的无人机目标跟踪算法［J］. 航空学报， 2020， 41（1）： 323423.
	LIU F， YANG A Z， WU Z W. Adaptive Siamese network based UAV target tracking algorithm［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（1）： 323423 （in Chinese）.
[3]	KUGARAJEEVAN J， KOKUL T， RAMANAN A， et al. Transformers in single object tracking： An experimental survey［J］. IEEE Access， 2023， 11： 80297-80326.
[4]	XIE F， WANG C Y， WANG G T， et al. Learning tracking representations via dual-branch fully Transformer networks［C］‍∥2021 IEEE/CVF International Conference on Computer Vision Workshops. Piscataway： IEEE Press， 2021： 2688-2697.
[5]	LIN L T， FAN H， ZHANG Z P， et al. SwinTrack： A simple and strong baseline for Transformer tracking［C］‍∥36th Conference on Neural Information Processing Systems. 2022： 16743-16754.
[6]	YE B T， CHANG H， MA B P， et al. Joint feature learning and relation modeling for tracking： A one-stream framework［C］‍∥European Conference on Computer Vision. Cham： Springer Nature Switzerland， 2022： 341-357.
[7]	YU B， TANG M， ZHENG L Y， et al. High-performance discriminative tracking with Transformers［C］‍∥2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE Press， 2021： 9836-9845.
[8]	WANG N， ZHOU W G， WANG J， et al. Transformer meets tracker： Exploiting temporal context for robust visual tracking［C］‍∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2021： 1571-1580.
[9]	CHEN X， YAN B， ZHU J W， et al. High-performance Transformer tracking［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2023， 45（7）： 8507-8523.
[10]	CUI Y T， JIANG C， WANG L M， et al. MixFormer： End-to-end tracking with iterative mixed attention［C］‍∥ 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2022： 13608-13618.
[11]	ZHOU X Y， WANG D Q， KRÄHENBÜHL P. Objects as points［DB/OL］. arXiv preprint： 1904.07850， 2019.
[12]	YAN B， PENG H W， FU J L， et al. Learning spatio-temporal Transformer for visual tracking［C］‍∥2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE Press， 2021： 10428-10437.
[13]	CAI Y D， LIU J， TANG J， et al. Robust object modeling for visual tracking［C］‍∥2023 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE Press， 2023： 9589-9600.
[14]	LAW H， DENG J. CornerNet： Detecting objects as paired keypoints［C］‍∥European Conference on Computer Vision. Cham： Springer Nature Switzerland， 2018： 765-781.
[15]	REZATOFIGHI H， TSOI N， GWAK J Y， et al. Generalized intersection over union： A metric and a loss for bounding box regression［C］‍∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2019： 658-666.
[16]	YAO Z W， GHOLAMI A， SHEN S， et al. ADAHESSIAN： An adaptive second order optimizer for machine learning［C］‍∥Proceedings of the AAAI Conference on Artificial Intelligence. 2021： 10665-10673.
[17]	WU Q Q， YANG T Y， LIU Z Q， et al. DropMAE： Masked autoencoders with spatial-attention dropout for tracking tasks［C］‍∥2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2023： 14561-14571.
[18]	DANELLJAN M， HÄGER G， SHAHBAZ KHAN F， et al. Accurate scale estimation for robust visual tracking［C］‍∥Proceedings of the British Machine Vision Conference 2014. Guildford： BMVA Press， 2014.
[19]	LIN T Y， MAIRE M， BELONGIE S， et al. Microsoft COCO： Common objects in context［C］‍∥Computer Vision-ECCV 2014. Cham： Springer International Publishing， 2014： 740-755.
[20]	HUANG L H， ZHAO X， HUANG K Q. GOT-10k： A large high-diversity benchmark for generic object tracking in the wild［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2021， 43（5）： 1562-1577.
[21]	FAN H， LIN L T， YANG F， et al. LaSOT： A high-quality benchmark for large-scale single object tracking［C］‍∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2019： 5369-5378.
[22]	MUELLER M， SMITH N， GHANEM B. A benchmark and simulator for UAV tracking［C］‍∥Computer Vision-ECCV 2016. Cham： Springer International Publishing， 2016： 445-461.
[23]	WU Y， LIM J， YANG M H. Online object tracking： A benchmark［C］‍∥2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2013： 2411-2418.
[24]	PAUWELS K， KRAGIC D. SimTrack： A simulation-based framework for scalable real-time object pose detection and tracking［C］‍∥2015 IEEE/RSJ International Conference on Intelligent Robots and System. Piscataway： IEEE Press， 2015： 1300-1307.
[25]	LI B， WU W， WANG Q， et al. SiamRPN++： Evolution of Siamese visual tracking with very deep networks［C］‍∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2019： 4277-4286.
[26]	GAO S Y， ZHOU C L， MA C， et al. AiATrack： Attention in attention for Transformer visual tracking［C］‍∥ Computer Vision-ECCV 2022. Cham： Springer Interational Publishing， 2022： 146-164.
[27]	CHEN X， YAN B， ZHU J W， et al. Transformer tracking［C］‍∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2021： 8122-8131.
[28]	FU Z H， FU Z H， LIU Q J， et al. SparseTT： Visual tracking with sparse Transformers［C］‍∥Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2022： 905-912.

跟踪算法	OTB100		UAV123		LaSOT		速度/（frame·s^-1）	参数量/10⁶
跟踪算法	AUC/%	P/%	AUC/%	P/%	AUC/%	P/%	速度/（frame·s^-1）	参数量/10⁶
所提算法	71.0	92.8	70.0	91.6	68.5	74.4	40	92
SimTrack-B		85.7	69.8	89.6	69.2	74.0	36	88
SwinTrack-B		90.2	64.9	84.3	67.2	70.8	45	90
TrDiMP	70.8	92.5	67.5	87.0	63.9	61.4	15	43
Stark	68.5	88.4	68.5	89.2	67.1		18	49
SparseTT	70.4	90.5	68.8	88.0	66.0	70.0	30	46
AiATrack	69.6	91.7	69.0	90.7	69.0	73.7	31	18
TransT	69.4	89.9	68.1	87.6	64.9	69.0	22	25
SiamRPN++	69.6	91.5	63.6	82.3	49.6	49.1	31	6

Adaptive template update-based Transformer algorithm for UAV target tracking

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 28

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Yiquan WU, Kang TONG. Research advances on deep learning-based small object detection in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 30848-030848.
[2]	Jiang ZHAO, Minghao PI, Bailing TIAN, Pei CHI, Yingxun WANG. Self-organized consensus decision-making method for swarm UAV tracking multiple targets [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(16): 331635-331635.
[3]	Haijun ZHANG, Qingyue XIA, Xu MA, Chao REN, Yang LU. A review of unmanned aerial vehicles deployment optimization in 6G low-altitude communication scenarios [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531296-531296.
[4]	Wenxiao HU, Di MU, Zhi LI, Yingyi GUO, Xinmin CHEN. Key technical issues and innovation strategies for development of low-altitude economy [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531539-531539.
[5]	Xudong LUO, Yiquan WU, Jinlin CHEN. Research progress on deep learning methods for object detection and semantic segmentation in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 28822-028822.
[6]	Gaojie ZHENG, Xiaoming HE, Dongpo LI, Huijun TAN, Kun WANG, Zhenlong WU, Depeng WANG. Double 90° deflection inlet/volute coupling flow characteristics of tail-powered unmanned aerial vehicle [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(4): 128782-128782.
[7]	Jian WANG, Lihui ZHOU, Jiafu CHEN, Xinqi LI, Linyang GUO, Zihao HE, Hao ZHOU. Time-varying smooth variable structure filter based on interactive multi-model [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(21): 330167-330167.
[8]	Jiang ZHAO, Xuan ZHANG, Pei CHI, Yingxun WANG. Self⁃adaptive formation control and dynamic path planning for air⁃ground heterogeneous swarm [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(16): 329809-329809.
[9]	Zhaochen CHU, Tao SONG, Ren JIN, Defu LIN. Vision-based air-to-air multi-UAVs tracking [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(14): 629379-629379.
[10]	Kunda LIU, Xueming LIU, Bo ZHU, Qingrui ZHANG. Robust safe control for multi⁃UAV formation flight through narrow corridors [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S2): 729768-729768.
[11]	Yi ZHANG, Yan ZHANG, Yu ZHANG, Yong ZHANG, Di LIU. Infrared aircraft target detection method based on multi-level feature enhancement fusion [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(22): 629220-629220.
[12]	Wei LI, Yan GUO, Ning LI, Cuntao LIU, Hao YUAN. Intelligent reflector surface assisted UAV mobile edge computing task data maximization method [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(19): 328486-328486.
[13]	Yubin YUAN, Yiquan WU, Langyue ZHAO, Jinlin CHEN, Qichang ZHAO. Research progress of UAV aerial video multi⁃object detection and tracking based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(18): 28334-028334.
[14]	Tianyu DU, Min WANG, Wenliang CHEN. Robust detection method of multi⁃type assembly reference hole based on monocular vision [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(12): 427852-427852.
[15]	CHEN Bo, YUE Kai, WANG Rusheng, HU Mingnan. Learning-based multi-rate multi-sensor fusion localization method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S1): 726904-726904.