Article(id=1281683247654482348, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.05.0701, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733760000000, receivedDateStr=2024-12-10, revisedDate=1740672000000, revisedDateStr=2025-02-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1783506698763, onlineDateStr=2026-07-08, pubDate=1748361600000, pubDateStr=2025-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783506698763, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783506698763, creator=13701087609, updateTime=1783506698763, updator=13701087609, issue=Issue{id=1281683189131359212, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='5', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1748361600000', pubDateStr='2025-05-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783506684811, creator='13701087609', updateTime=1783589333681, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282029843684504545, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282029843684504546, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=161, endPage=168, ext={EN=ArticleExt(id=1281683249365758381, articleId=1281683247654482348, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Research on cross-visual pedestrian monitoring based on virtual simulation in buildings, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to solve the problems of the high cost of multi-channel video data collection and long-term high-quality annotation in high-rise buildings or complex open building environments, the generation of multi-channel video data across the field of view and the automatic annotation of pedestrian images was realized. Firstly, a virtual reality scene was designed to simulate pedestrian movement and automatically obtain marker data. Secondly, unsupervised domain adaptation methods were researched to reduce the difference in feature distribution between source and target domain data, enabling the model to generalize to the target building scene. Finally, the model's generalization ability was verified. Results show that the constructed virtual reality scene effectively overcomes the difficulties of cross-visual video data collection and high-quality annotation. The unsupervised domain adaptation method increased the average first hit rate from 22.02% to 45.48%. By combining source domain style conversion, data augmentation, and target domain pseudo label generation, the first hit rate has been increased by 20%, reducing distribution bias and achieving generalization of the model in different building scenarios.

, authors=Zhenxiang TAO¹, Ying LI¹, Xubo HUANG¹, Yisen WANG¹, Ping ZHANG², Rui YANG², authorsList=Zhenxiang TAO, Ying LI, Xubo HUANG, Yisen WANG, Ping ZHANG, Rui YANG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281683249722274222, articleId=1281683247654482348, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=建筑内基于虚拟仿真的跨视域行人监测研究, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为解决高层建筑楼宇或复杂开放建筑环境中多路视频数据采集成本高、长时间高质量标注难等问题，实现跨视域多路视频数据的生成与行人图像的自动标注，首先，设计虚拟现实场景，模拟行人运动并自动获取标记数据；其次，研究无监督领域自适应方法，降低源域数据与目标域数据的特征分布差异，使模型泛化至目标建筑场景；最后，验证模型泛化能力。研究结果表明：构建的虚拟现实场景能有效克服跨视域视频数据采集与高质量标注的困难；无监督领域自适应方法将平均首位命中率从22.02%提升至45.48%；结合源域风格转换、数据增广和目标域伪标签生成，首位命中率提升20%，降低了分布偏差，有助于实现模型在不同建筑场景中的泛化。

, authors=陶振翔讲师¹, 李滢¹, 黄绪勃¹, 王一森¹, 张平², 杨锐副研究员², authorsList=陶振翔讲师, 李滢, 黄绪勃, 王一森, 张平, 杨锐副研究员, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=oWQxIfqd0nnusPlgwDJB1Q==, magXml=vpnjv/WaMvM0243OU/AAPQ==, pdfUrl=null, pdf=/6eX0cHUOvmQRArBCdR7tw==, pdfFileSize=4770781, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Q6VFJc/Jn/yFIr+Pvhk+9Q==, mapNumber=null, fund=null)}, authors=[Author(id=1281683250116538806, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683247654482348, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=ztao@cumtb.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281683250192036280, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683247654482348, authorId=1281683250116538806, language=EN, stringName=Zhenxiang TAO, firstName=Zhenxiang, middleName=null, lastName=TAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281683250275922361, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683247654482348, authorId=1281683250116538806, language=CN, stringName=陶振翔讲师, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹中国矿业大学（北京） 应急管理与安全工程学院，北京 100083, bio={"content":"

陶振翔（1990—），男，陕西渭南人，博士，讲师，主要从事消防应急疏散等方面的研究。E-mail: ztao@cumtb.edu.cn。

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陶振翔（1990—），男，陕西渭南人，博士，讲师，主要从事消防应急疏散等方面的研究。E-mail: ztao@cumtb.edu.cn。

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