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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.


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Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems

Show Author's information Ling HuQiang 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
Received: 30 December 2017 Accepted: 03 January 2018 Published: 12 April 2018 Issue date: June 2018
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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.

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