Page 23 - Read Online

Xu et al. J Surveill Secur Saf 2020;1:16-33 Journal of Surveillance,
DOI: 10.20517/jsss.2020.04 Security and Safety

Original Article Open Access

Big data analytics of crime prevention and control
based on image processing upon cloud computing

Zheng Xu , Cheng Cheng , Vijayan Sugumaran 3
1,2
1
1 School of Computer Science, Shanghai University, Shanghai 201142, China.
2 Cernter of IoT, The third research institute of the ministry of public security, Shanghai 200335, China.
3 Department of Decision and Information Sciences, Oakland University, Rochester, MI 48309, USA.

Correspondence to: Prof. Zheng Xu, School of Computer Science, Shanghai University, Shanghai 201142, China.
E-mail: zhengxu@shu.edu.cn
How to cite this article: Xu Z, Cheng C, Sugumaran V. Big data analytics of crime prevention and control based on image
processing upon cloud computing. J Surveill Secur Saf 2020;1:16-33. http://dx.doi.org/10.20517/jsss.2020.04
Received: 5 Mar 2020 First Decision: 21 Apr 2020 Revised: 5 Jun 2020 Accepted: 12 Aug 2020 Available online: 12 Sep 2020

Academic Editor: Yelena Yesha Copy Editor: Cai-Hong Wang Production Editor: Jing Yu

Abstract
Aim: Current crime behavior observation has the problem of not being real time, thus criminal behavior cannot be
promptly controlled. To improve the control of criminal behavior, this study was based on cloud computing image
processing, and adopted data mining for criminal behavior.

Methods: This study obtained many criminal behavior characteristics through data collection and combined the
rapid response capability of cloud computing to adopt data processing. In addition, to improve the accuracy of
criminal behavior recognition, the identification method for criminal behaviors in selected populations was studied,
and the image processing technology was combined to identify individual crimes and subject segmentation.

Results: Our work used statistical methods to collect the characteristics of criminal behavior, and we designed
experiments to verify the effectiveness of the algorithm. The experimental research shows that the algorithm has
high accuracy in identifying abnormal behavior.

Conclusion: The research shows that the accuracy of the algorithm for identifying abnormal behavior is relatively
high, and it has high practical value, which can meet the accuracy and real-time requirements of security systems.

Keywords: Cloud computing, cloud storage, data mining, crime prevention and control, image capture

© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made.

www.jsssjournal.com