Journal Home > Volume 1 , Issue 2
Big Data Mining and Analytics 2018, 1(2): 137-145 https://doi.org/10.26599/BDMA.2018.9020013
Open Access | Issue | Published: 12 April 2018

Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems

Show Author's Information Ling Hu,  Qiang Ni( ),  Feng Yuan
School of Computing and Communications, Lancaster University, InfoLab21, Lancaster, LA1 4WA.
Chinese Academy of Sciences Smart City Software Co. Ltd., China and also with Institute of Software Application Technology, Guangzhou & Chinese Academy of Sciences, Guangzhou 511458, China.
Keywords:
big data, background subtraction, urban surveillance systems
Cite this article:
Hu L, Ni Q, Yuan F. Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems. Big Data Mining and Analytics, 2018, 1(2): 137-145. https://doi.org/10.26599/BDMA.2018.9020013
Download citation
EndNote(RIS)
BibTeX

633

Views

35

Downloads

Citations

7

Crossref

1

WoS

9

Scopus

0

CSCD

Abstract Full text About this article

Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.


menu
Abstract
Full text
Outline
About this article

Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems

Show Author's information Ling Hu, Qiang Ni( ), Feng Yuan
School of Computing and Communications, Lancaster University, InfoLab21, Lancaster, LA1 4WA.
Chinese Academy of Sciences Smart City Software Co. Ltd., China and also with Institute of Software Application Technology, Guangzhou & Chinese Academy of Sciences, Guangzhou 511458, China.

Abstract

Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.

Keywords: big data, background subtraction, urban surveillance systems

References(11)

[1]
M. V. Moreno, F. Terroso-Senz, A. Gonzlez-Vidal, M. Valds-Vela, A. F. Skarmeta, M. A. Zamora, and V. Chang, Applicability of big data techniques to smart cities deployments, IEEE Transactions on Industrial Informatics, vol. 13, no. 2, pp. 800-809, 2017.
DOI Google Scholar
[2]
M. M. Rathore, A. Ahmad, and A. Paul, IoT-based smart city development using big data analytical approach, in 2016 IEEE International Conference on Automatica (ICAACCA), 2016, pp. 1-8.
DOI
[3]
S. Chen, H. Xu, D. Liu, B. Hu, and H. Wang, A vision of IoT: Applications, challenges, and opportunities with China perspective, IEEE Internet of Things Journal, vol. 1, no. 4, pp. 349-359, 2014.
DOI Google Scholar
[4]
M. Handte, S. Foell, S. Wagner, G. Kortuem, and P. Marrn, An internet-of-things enabled connected navigation system for urban bus riders, IEEE Internet of Things Journal, vol. 3, no. 5, pp. 735-744, 2016.
DOI Google Scholar
[5]
H. Liu and X. Hou, Moving detection research of background frame difference based on gaussian model, in 2012 International Conference on Computer Science and Service System, 2012, pp. 258-261.
DOI
[6]
X. Han, Y. Gao, and Z. Lu, Research on moving object detection algorithm based on improved three frame difference method and optical flow, in Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control, 2015.
DOI
[7]
Y. Zhuang, C. Wu, Y. Zhang, and S. Feng, Detection and tracking algorithm based on frame difference method and particle filter algorithm, in 2017 29th Chinese Control and Decision Conference (CCDC), 2017, pp. 161-166.
[8]
J. Suhr, H. Jung, G. Li, and J. Kim, Mixture of Gaussians-based background subtraction for bayer-pattern image sequences, IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 3, pp. 365-370, 2011.
DOI Google Scholar
[9]
D. Mukherjee, Q. Wu, and T. Nguyen, Gaussian mixture model with advanced distance measure based on support weights and histogram of gradients for background suppression, IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1086-1096, 2014.
DOI Google Scholar
[10]
V. Sikri, Proposition and comprehensive efficiency evaluation of a foreground detection algorithm based on optical flow and canny edge detection for video surveillance systems, in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016, pp. 1466-1472.
DOI
[11]
L. Hu and Q. Ni, IoT-driven automated object detection algorithm for urban surveillance systems in smart cities, IEEE Internet of Things Journal, .
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 30 December 2017
Accepted: 03 January 2018
Published: 12 April 2018
Issue date: June 2018

Copyright

© The author(s) 2018

Acknowledgements

This work was supported by the projects under the grants Nos. 2016B050502001 and 2015A050502003.

Rights and permissions

Return