全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2018 

基于E-V融合的线上-线下联合监控技术
Online-offline associated surveillance system based on E-V fusion

DOI: 10.16511/j.cnki.qhdxxb.2018.26.019

Keywords: 数据融合,社会安全,WiFi信号,视频分析,
data fusion
,social security,WiFi signal,video analyze

Full-Text   Cite this paper   Add to My Lib

Abstract:

近期在世界各地发生的群体性事件、恐怖事件等社会安全事件对社会安全管理提出了新的挑战。在警力资源有限的情况下,如何有效处理这类重大突发事件就成为公共安全研究的一个重要课题。针对现存的安全问题,基于线上-线下多源信息联合监控方案,该文提出一种基于E-V融合的信息融合系统,该系统同时采集手机信号数据(E数据)和监控摄像头数据(V数据),并通过数据融合从两方面获取人的位置与身份信息。通过实验室测试,结果表明:该系统能够在少数人情况下完成视频的人物位置信息、手机MAC(media access control)地址、手机接收信号强度(receive signal strength,RSS)信息的融合,完成作为线上信息与线下信息结合点的功能。
Abstract:Recent mass disturbances and terrorist attacks have presented new challenges for public management. Thus, more research is need on how to cope with serous public security incident with limited police forces. An novel online-offline public security incident surveillance system is described here with an E-V information fusion system. The system collects surveillance video data and cellphone data to identify human locations and IDs. Tests show that the system can combine human locations in videos, cellphone MAC addresses and cellphone RSS data in situations with few people by combing online and offline information.

References

[1]  CURRIER C, GREENWALD G, FISHMAN A. U.S. Government designated prominent al Jazeera journalist as "member of Al Qaeda"[N/OL].(2015G05G08)[2017G10G25]. https://theintercept.com/2015/05/08/u-s-government-designated-prominent-al-jazeera-journalist-al-qaeda-member-put-watch-list/.
[2]  TENG J, ZHANG B Y, ZHU J D, et al. EV-Loc:Integrating electronic and visual signals for accurate localization[J]. IEEE/ACM Transactions on Networking, 2014, 22(4):1285-1296.
[3]  ZHU J D, TENG J, XUAN D, et al. Effective visual tracking with electronic localization by directional antennas[C]//Proceedings of 2011 IEEE National Aerospace and Electronics Conference. Dayton, USA:IEEE, 2011:95-100.
[4]  LI X F, TENG J, ZHAI Q, et al. EV-Human:Human localization via visual estimation of body electronic interference[C]//Proceedings of 2013 IEEE INFOCOM. Turin, Italy:IEEE, 2013:500-504.
[5]  马国富, 王子贤, 马胜利. 机器学习模型在预测服刑人员再犯罪危险性中的效用分析[J]. 河北大学学报(自然科学版), 2017, 37(4):426-433. MA G F, WANG Z X, MA S L. Analysis of the effectiveness of machine learning model in predicting the risk of inmates[J]. Journal of Hebei University (Natural Science Edition), 2017, 37(4):426-433. (in Chinese)
[6]  刘莹, 王宁, 李保华, 等. 模糊语法方法在犯罪文本分类中的应用[J]. 计算机工程与设计, 2017, 38(7):1965-1971. LIU Y, WANG N, LI B H, et al. Application of fuzzy grammar method in crime text classification[J]. Computer Engineering and Design, 2017, 38(7):1965-1971. (in Chinese)
[7]  吕雪梅. 美国预测警务中基于大数据的犯罪情报分析[J]. 情报杂志, 2015, 34(12):16-20. Lü X M. Surveying the crime analysis in U.S. prediction policing from big data[J]. Journal of Intelligence, 2015, 34(12):16-20. (in Chinese)
[8]  于红志, 刘凤鑫, 邹开其. 改进的模糊BP神经网络及在犯罪预测中的应用[J]. 辽宁工程技术大学学报(自然科学版), 2012, 31(2):244-247. YU H Z, LIU F X, ZOU K Q. Improved fuzzy BP neural network and its application in crime prediction[J]. Journal of Liaoning Technical University (Natural Science), 2012, 31(2):244-247. (in Chinese)
[9]  王雨晨, 过仲阳, 王媛媛. 基于随机森林的犯罪风险预测模型研究[J]. 华东师范大学学报(自然科学版), 2017(4):89-96. WANG Y C, GUO Z Y, WANG Y Y. A forecasting model of crime risk based on random forest[J]. Journal of East China Normal University (Natural Science), 2017(4):89-96. (in Chinese)
[10]  DHAKA P, JOHARI R. CRIB:Cyber crime investigation, data archival and analysis using big data tool[C]//Proceedings of 2016 International Conference on Computing, Communication and Automation. Noida, India:IEEE, 2016:117-121.
[11]  吕林涛, 姬娜, 张九龙. 基于RBF神经网络的可疑交易监测模型[J]. 计算机工程与应用, 2010, 46(3):207-210. Lü L T, JI N, ZHANG J L. Suspicious transaction detection model based on Radial Basis Function Neural Network[J]. Computer Engineering and Applications, 2010, 46(3):207-210. (in Chinese)
[12]  KHAN S, ANSARI F, DHALVELKAR H A, et al. Criminal investigation using call data records (CDR) through big data technology[C]//Proceedings of 2017 International Conference on Nascent Technologies in Engineering. Navi Mumbai, India:IEEE, 2017:1-5.
[13]  WEINSTEIN C, CAMPBELL W, DELANEY B, et al. Modeling and detection techniques for counter-terror social network analysis and intent recognition[C]//Proceedings of 2009 IEEE Aerospace Conference. Big Sky, USA:IEEE, 2009:1-16.
[14]  李泽, 孙多勇, 李博. 基于社会网络与事件关联的恐怖事件监测与识别[J]. 科技导报, 2017, 35(9):87-94. LI Z, SUN D Y, LI B. Terrorist events monitoring and identifying based on correlation between social networks and events[J]. Science & Technology Review, 2017, 35(9):87-94. (in Chinese)

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133