UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism
Pages 366 - 371
Abstract
To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene.
References
[1]
Yiqiao. Xu, Zhanyue. Zhang, Sitong. Liu and Gang. Zhang, “Reconnaissance timeliness analysis method for remote sensing satellite based on queuing model,” Command Information System and Technology, Vol. 13, No. 1, pp. 84-89, 2022.
[2]
Jianqiang. Liu, Xiaomin. Ye and Youguo Lan, “Remote sensing big data from Chinese ocean satellites and its application service,” Big Data Research, Vol. 8, No. 2, pp. 75-88, 2022.
[3]
Jing. Ren, “Application of UAV remote sensing technology in engineering mappinng,” Nonferrous Metals Design, Vol. 49, No. 1, pp. 66-69, 2022.
[4]
Jin. Xiong and Yifeng. Niu, "Guidance law for multi-UAVs collaborative ground target tracking under obstacle environment," 2017 29th Chinese Control and Decision Conference (CCDC), 2017, pp. 7219-7223.
[5]
Jingxuan. Hao, Yimin. Zhou, Guoshan. Zhang, Qin. Lv and Qingtian. Wu, "A Review of Target Tracking Algorithm Based on UAV," 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), 2018, pp. 328-333.
[6]
Zirui. Zhang and Jun. Cheng, "Multi-Camera Tracking Helmet System," Journal of Image and Graphics, Vol. 1, No. 2, pp. 76-79, June 2013.
[7]
Jorge Henrique Busatto Casagrande and Marcelo Ricardo Stemmer, "Abnormal Motion Analysis for Tracking-Based Approaches Using Region-Based Method with Mobile Grid," Journal of Image and Graphics, Vol. 2, No. 1, pp. 22-27, June 2014.
[8]
Saad A. Yaseen and Sreela Sasi, "Robust Algorithm for Object Detection and Tracking in a Dynamic Scene," Journal of Image and Graphics, Vol. 2, No. 1, pp. 41-45, June 2014.
[9]
Karthik Dinesh and Sumana Gupta, "Video Stabilization, Camera Motion Pattern Recognition and Motion Tracking Using Spatiotemporal Regularity Flow," Journal of Image and Graphics, Vol. 2, No. 1, pp. 33-40, June 2014.
[10]
Chin-Shiuh Shieh, Yong-Shixa Jhan, Yuan-Li Liu, Mong-Fong Horng, and Tsair-Fwu Lee, "Video Object Tracking with Heuristic Optimization Methods," Journal of Image and Graphics, Vol. 6, No. 2, pp. 95-99, December 2018.
[11]
David S. Bolme, J. Ross Beveridge, Bruce A. Draper and Yuiman Lui, "Visual object tracking using adaptive correlation filters," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2544-2550.
[12]
Qianyun. Du, Zhaoquan. Cai, Hao. Liu and Zhuliang. Yu, "A rotation adaptive correlation filter for robust tracking," 2015 IEEE International Conference on Digital Signal Processing (DSP), 2015, pp. 1035-1038.
[13]
Chao. Ma, Jiabin. Huang, Xiaokang. Yang and Minghsuan. Yang, "Robust Visual Tracking via Hierarchical Convolutional Features," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 11, pp. 2709-2723, 1 Nov. 2019.
[14]
Shan. Jiang, Shuixiao. Li and Chengfei. Zhu, "Kernelized Correlation Filter Tracking with Scale Adaptive Filter and Feature Integration," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018, pp. 201-206.
[15]
Ran. Tao, Efstratios. Gavves and Arnold. W. M. Smeulders, "Siamese Instance Search for Tracking," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1420-1429.
[16]
Zhongliang. Wei and Xiaohui. Yang, "Object tracking algorithm based on fusion of SiamFC and Feature Pyramid Network," 2021 International Conference on Internet, Education and Information Technology (IEIT), 2021, pp. 244-248.
[17]
Dongdong. Li, Gongjian. Wen, Yangliu. Kuai and Fatih. Porikli, "End-to-End Feature Integration for Correlation Filter Tracking With Channel Attention," in IEEE Signal Processing Letters, Vol. 25, No. 12, pp. 1815-1819, Dec. 2018.
[18]
Qing. Guo, Wei. Feng, Ce. Zhou, Rui. Huang, Liang. Wan and Song. Wang, "Learning Dynamic Siamese Network for Visual Object Tracking," 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1781-1789.
[19]
Lei. Pu, Xinxi. Feng, Zhiqiang. Hou, Wangsheng. Yu, Yufei. Zha, “SiamDA: Dual attention Siamese network for real-time visual tracking,” in Signal Processing: Image Communication, Vol. 95, 2021.
[20]
Zhipeng. Zhang and Houwen. Peng, "Deeper and Wider Siamese Networks for Real-Time Visual Tracking," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4586-4595.
[21]
Jing. Huang, Lifeng. Ren, Xiaokang. Zhou and Ke. Yan, "An Improved Neural Network Based on SENet for Sleep Stage Classification," in IEEE Journal of Biomedical and Health Informatics.
Index Terms
- UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism
Recommendations
Multi-Camera Tracking and Mapping for Unmanned Aerial Vehicles in Unstructured Environments
Pose estimation for small unmanned aerial vehicles has made large improvements in recent years, leading to vehicles that use a suite of sensors to navigate and explore various environments. In particular, cameras have become popular due to their low ...
Twofold Siamese Network with Attentional Feature Fusion for Object Tracking
ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial IntelligenceObject tracking is still a critical and challenging problem in computer vision. More and more researchers pay attention to applying deep learning to obtain the powerful feature for robust tracking. Nowadays, feature fusion is an essential part of ...
Automatic Take Off, Tracking and Landing of a Miniature UAV on a Moving Carrier Vehicle
We present a system consisting of a miniature unmanned aerial vehicle (UAV) and a small carrier vehicle, in which the UAV is capable of autonomously starting from the moving ground vehicle, tracking it at a constant distance and landing on a platform on ...
Comments
Information & Contributors
Information
Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Graduate Innovation Fund of Xi'an University of Posts and Telecommunications
- National Natural Science Foundation of China
- Key Research and Development Project of Shaanxi Province
Conference
AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 29Total Downloads
- Downloads (Last 12 months)13
- Downloads (Last 6 weeks)0
Reflects downloads up to 23 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format