Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

Author:

Li Zhize,Liu Jun,Chen Kezhou,Gao Xiang,Tang Chenshuo,Xie Chao,Lu XuORCID

Abstract

Abstract Target-tracking applications are promising and possess great theoretical and practical significance, though the research faces great challenges. With the development of multi-modal depth-sensing technology, a large number of scholars have proposed various target-tracking methods based on heterogeneous sensing and demonstrated great results. This review provides an overview of the techniques involved in target tracking in the different layers of the network as well as a comprehensive analysis of the research progress in heterogeneous sensing techniques in each layer. First, this review introduces the single sensing scheme and heterogeneous sensing scheme in the physical layer. Second, we present the heterogeneous communication technologies and heterogeneous optimization methods for communication protocols in the network layer. Third, we combine several typical heterogeneous-sensor target-tracking applications and analyze the applications of cloud computing, edge computing, big data and blockchain technologies. Finally, we discuss the challenges and future direction of heterogeneous-sensor target-tracking methods.

Funder

Key Project of Guangdong

Province Basic Research Foundation

National Natural Science Foundation of China

Scientific and Technological Planning Project of Guangzhou

Project Supported by Guangdong Province Universities

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Reference213 articles.

1. Current status of research on visual tracking technology and its outlook;Li;Comput. Appl. Res.,2010

2. Hierarchical convolutional features for visual tracking;Yang,2015

3. Learning multi-domain convolutional neural networks for visual tracking;Nam,2016

4. Fully-convolutional siamese networks for object tracking;Bertinetto,2016

5. Supplementary material ECO: efficient convolution operators for tracking;Danelljan,2017

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Measurement Science and Technology’s second century underway;Measurement Science and Technology;2023-09-27

2. A disturbance rejection adaptive filtering approach for human motion tracking *;Measurement Science and Technology;2023-09-13

3. Enhanced Routing Capabilities for WSN Using QOS Optimization Technique;2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON);2023-08-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3