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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Friday 06 December 2019

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Background Modelling and Subtraction in High Level Computer Vision Application with Security


Volume 4, Issue 2, July 2014, Pages 70–76

 Background Modelling and Subtraction in High Level Computer Vision Application with Security

D. Balasubramanian and V. Saranraj

Original language: English

Received 12 May 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Intelligent video surveillance system deal's with the real-time monitoring of persistent and transient objects within a specific environment. In existing video surveillance using CCTV (close circuit television) works with binary segmentation algorithm and it had critical pre processing steps in various high level computer vision application. This can be applied not only in security systems, but also uses in environmental surveillance. The basic principle of moving object detecting is given by the Background Subtraction algorithm. Then, a self-adaptive background model that can update automatically and timely to adapt to the slow and slight changes of natural environment is detailed. When the subtraction of the current captured image and the background reaches a certain threshold, a moving object is considered to be in the current view, and the mobile phone will automatically notify the central control unit and automatic alerting system alert the authorized user through SMS and user can view the detected image by GPRS enabled mobile devices.

Author Keywords: Background Modelling and Subtraction, GPRS, SMS, surveillance.


How to Cite this Article


D. Balasubramanian and V. Saranraj, “Background Modelling and Subtraction in High Level Computer Vision Application with Security,” International Journal of Innovation and Scientific Research, vol. 4, no. 2, pp. 70–76, July 2014.