中国科学院长春光学精密机械与物理研究所

QQ客服 官方微博 反馈留言

	Interframe Saliency Transformer and Lightweight Multidimensional Attention Network for Real-Time Unmanned Aerial Vehicle Tracking
	A. Deng, G. Han, D. Chen, T. Ma, X. Wei and Z. Liu
	2023
发表期刊	Remote Sensing
ISSN	20724292
卷号	15期号:17
摘要	UAV visual-object-tracking technology based on Siamese neural networks has great scientific research and practical application value, and it is widely used in geological surveying, reconnaissance monitoring, and environmental monitoring. Due to the limited onboard computational resources and complex real-world environments of drones, most of the existing tracking systems based on Siamese neural networks struggle to combine excellent performance with high efficiency. Therefore, the key issue is to study how to improve the accuracy of target tracking under the challenges of real-time performance and the above factors. In response to this problem, this paper proposes a real-time UAV tracking system based on interframe saliency transformer and lightweight multidimensional attention network (SiamITL). Specifically, interframe saliency transformer is used to continuously perceive spatial and temporal information, making the network more closely related to the essence of the tracking task. Additionally, a lightweight multidimensional attention network is used to better capture changes in both target appearance and background information, improving the ability of the tracker to distinguish between the target and background. SiamITL is effective and efficient: extensive comparative experiments and ablation experiments have been conducted on multiple aerial tracking benchmarks, demonstrating that our algorithm can achieve more robust feature representation and more accurate target state estimation. Among them, SiamITL achieved success and accuracy rates of 0.625 and 0.818 in the UAV123 benchmark, respectively, demonstrating a certain level of leadership in this field. Furthermore, SiamITL demonstrates the potential for real-time operation on the embedded platform Xavier, highlighting its potential for practical application in real-world scenarios. © 2023 by the authors.
DOI	10.3390/rs15174249
URL	查看原文
收录类别	ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67425
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	A. Deng, G. Han, D. Chen, T. Ma, X. Wei and Z. Liu. Interframe Saliency Transformer and Lightweight Multidimensional Attention Network for Real-Time Unmanned Aerial Vehicle Tracking[J]. Remote Sensing,2023,15(17).
APA	A. Deng, G. Han, D. Chen, T. Ma, X. Wei and Z. Liu.(2023).Interframe Saliency Transformer and Lightweight Multidimensional Attention Network for Real-Time Unmanned Aerial Vehicle Tracking.Remote Sensing,15(17).
MLA	A. Deng, G. Han, D. Chen, T. Ma, X. Wei and Z. Liu."Interframe Saliency Transformer and Lightweight Multidimensional Attention Network for Real-Time Unmanned Aerial Vehicle Tracking".Remote Sensing 15.17(2023).

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Interframe Saliency （7001KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览 下载

个性服务

推荐该条目

保存到收藏夹

查看访问统计

导出为Endnote文件

谷歌学术

谷歌学术中相似的文章

[A. Deng, G. Han, D. Chen, T. Ma, X. Wei and Z. Liu]的文章

百度学术

百度学术中相似的文章

[A. Deng, G. Han, D. Chen, T. Ma, X. Wei and Z. Liu]的文章

必应学术

必应学术中相似的文章

[A. Deng, G. Han, D. Chen, T. Ma, X. Wei and Z. Liu]的文章

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。