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题名

A Reinforcement Learning Based Design of Compressive Sensing Systems for Human Activity Recognition

作者
通讯作者Hao, Qi
DOI
发表日期
2018
ISSN
21689229
ISBN
978-1-5386-4708-0
会议录名称
卷号
2018-October
页码
1456-1459
会议日期
28-31 Oct. 2018
会议地点
New Delhi, India
出版地
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者
摘要
This paper presents a reinforcement learning based distributed compressive sensing system design method for human activity recognition. This system uses distributed infrared sensors to capture human motion information and aims at representing complex activity scenarios with as little amount of data as possible. Therefore, a set of binary sampling masks are designed to modulate the fields of view (FoV) of sensors and to reduce the amount of measurement data without losing the major features of the target information. The spatial relation between two adjacent sensors is investigated to acquire 3d information with the maximum efficiency. In this work, the main contributions include two parts: (1) design the optimal deployment of distributed sensors and (2) learn the structure of sampling masks by using the policy gradient (PG) based reinforcement learning scheme. Experiment results show that the the proposed system can increase the sensing efficiency and improve the performance of activity recognition.
关键词
学校署名
通讯
语种
英语
相关链接[来源记录]
收录类别
资助项目
National Natural Science Foundation of China[61773197]
WOS研究方向
Engineering ; Remote Sensing
WOS类目
Engineering, Electrical & Electronic ; Remote Sensing
WOS记录号
WOS:000468199300374
EI入藏号
20190606463693
EI主题词
Compressed sensing ; Design ; Efficiency ; Infrared detectors ; Machine learning ; Pattern recognition
EI分类号
Information Theory and Signal Processing:716.1 ; Artificial Intelligence:723.4 ; Production Engineering:913.1 ; Radiation Measuring Instruments:944.7
来源库
Web of Science
全文链接https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8589690
引用统计
被引频次[WOS]:0
成果类型会议论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/24621
专题工学院_计算机科学与工程系
前沿与交叉科学研究院
作者单位
1.Harbin Inst Technol, Dept Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
通讯作者单位计算机科学与工程系
推荐引用方式
GB/T 7714
Liu, Guocheng,Ma, Rui,Hao, Qi. A Reinforcement Learning Based Design of Compressive Sensing Systems for Human Activity Recognition[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:1456-1459.
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