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Journal of Electronic Imaging • new

Comparative study of motion detection methods for video surveillance systems
Author(s): Kamal Sehairi; Fatima Chouireb; Jean Meunier
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Paper Abstract

The objective of this study is to compare several change detection methods for a monostatic camera and identify the best method for different complex environments and backgrounds in indoor and outdoor scenes. To this end, we used the CDnet video dataset as a benchmark that consists of many challenging problems, ranging from basic simple scenes to complex scenes affected by bad weather and dynamic backgrounds. Twelve change detection methods, ranging from simple temporal differencing to more sophisticated methods, were tested and several performance metrics were used to precisely evaluate the results. Because most of the considered methods have not previously been evaluated on this recent large scale dataset, this work compares these methods to fill a lack in the literature, and thus this evaluation joins as complementary compared with the previous comparative evaluations. Our experimental results show that there is no perfect method for all challenging cases; each method performs well in certain cases and fails in others. However, this study enables the user to identify the most suitable method for his or her needs.

Paper Details

Date Published: 25 April 2017
PDF: 29 pages
J. Electron. Imaging. 26(2) 023025 doi: 10.1117/1.JEI.26.2.023025
Published in: Journal of Electronic Imaging Volume 26, Issue 2
Show Author Affiliations
Kamal Sehairi, Univ. of Laghouat Amar Telidji (Algeria)
Fatima Chouireb, Univ. of Laghouat Amar Telidji (Algeria)
Jean Meunier, Univ. de Montréal (Canada)


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