RGB-thermal Imaging System Collaborated with Marker Tracking for Remote Breathing Rate Measurement

Lushuang Chen, Ning Liu, Menghan Hu, Guangtao Zhai
{"title":"RGB-thermal Imaging System Collaborated with Marker Tracking for Remote Breathing Rate Measurement","authors":"Lushuang Chen, Ning Liu, Menghan Hu, Guangtao Zhai","doi":"10.1109/VCIP47243.2019.8965987","DOIUrl":null,"url":null,"abstract":"The pixel variation signal extracted from the nasal region of RGB-thermal images can be used to achieve breathing rate (BR) measurement. However, this method fails when the nasal region is not detected in complicated motion scenarios. In this paper, we develop an RGB-thermal imaging system collaborated with marker sticker to achieve unobtrusive and accurate BR measurement. Pixel variation signal of Regions of interest (ROI) is extracted from the thermal video and chest movement signal is extracted from the RGB video with the assistance of marker stickers. Subsequently, a custom-made time-domain signal processing approach is developed for determining BR. We further propose a method of splicing computation to measure the BR after separate processing of signal segments. We construct an RGB-thermal video dataset with different head and body movements to evaluate the effectiveness of the proposed algorithm. After linear regression analysis, the determination coefficient (R2) of 0.905 has been observed for the estimated and reference BRs, indicating the feasibility of our proposed method in complex motion scenarios.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Visual Communications and Image Processing (VCIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VCIP47243.2019.8965987","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

The pixel variation signal extracted from the nasal region of RGB-thermal images can be used to achieve breathing rate (BR) measurement. However, this method fails when the nasal region is not detected in complicated motion scenarios. In this paper, we develop an RGB-thermal imaging system collaborated with marker sticker to achieve unobtrusive and accurate BR measurement. Pixel variation signal of Regions of interest (ROI) is extracted from the thermal video and chest movement signal is extracted from the RGB video with the assistance of marker stickers. Subsequently, a custom-made time-domain signal processing approach is developed for determining BR. We further propose a method of splicing computation to measure the BR after separate processing of signal segments. We construct an RGB-thermal video dataset with different head and body movements to evaluate the effectiveness of the proposed algorithm. After linear regression analysis, the determination coefficient (R2) of 0.905 has been observed for the estimated and reference BRs, indicating the feasibility of our proposed method in complex motion scenarios.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
rgb热成像系统与远程呼吸率测量的标记跟踪合作
从rgb热图像中提取的鼻区像素变化信号可用于呼吸速率的测量。然而,当在复杂的运动场景中没有检测到鼻腔区域时,这种方法就失败了。在本文中,我们开发了一种rgb热成像系统与标记贴纸协作,以实现不显眼和准确的BR测量。在标记贴纸的辅助下,从热视频中提取感兴趣区域(ROI)的像素变化信号,从RGB视频中提取胸部运动信号。随后,开发了一种定制的时域信号处理方法来确定BR。我们进一步提出了一种拼接计算方法来测量信号段分离处理后的比。我们构建了一个具有不同头部和身体运动的rgb热视频数据集来评估所提出算法的有效性。经线性回归分析,估计BRs和参考BRs的决定系数R2均为0.905,表明本文方法在复杂运动场景下是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy Part-guided Network for Pedestrian Attribute Recognition Feature Map Alignment: Towards Efficient Design of Mixed-precision Quantization Scheme VCIP 2019 Organizing Committee Weather Data Integrated Mask R-CNN for Automatic Road Surface Condition Monitoring
wanfang
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1