Special Section on Perceptually Driven Visual Information Analysis

Moving object detection using a background modeling based on entropy theory and quad-tree decomposition

[+] Author Affiliations
Omar Elharrouss, Driss Moujahid, Hamid Tairi

University of Sidi Mohamed Ben Abdellah, Department of Informatics, Faculty of Science Dhar-Mahraz, P.B 1796 Atlas-Fez, Morocco

Samah Elkah

University of Sidi Mohamed Ben Abdellah, Department of Physics, Faculty of Science Dhar-Mahraz, P.B 1796 Atlas-Fez, Morocco

J. Electron. Imaging. 25(6), 061615 (Nov 22, 2016). doi:10.1117/1.JEI.25.6.061615
History: Received April 21, 2016; Accepted October 27, 2016
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Abstract.  A particular algorithm for moving object detection using a background subtraction approach is proposed. We generate the background model by combining quad-tree decomposition with entropy theory. In general, many background subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modeling approach analyzes the illumination change problem. After performing the background subtraction based on the proposed background model, the moving targets can be accurately detected at each frame of the image sequence. In order to produce high accuracy for the motion detection, the binary motion mask can be computed by the proposed threshold function. The experimental analysis based on statistical measurements proves the efficiency of our proposed method in terms of quality and quantity. And it even outperforms substantially existing methods by perceptional evaluation.

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© 2016 SPIE and IS&T

Citation

Omar Elharrouss ; Driss Moujahid ; Samah Elkah and Hamid Tairi
"Moving object detection using a background modeling based on entropy theory and quad-tree decomposition", J. Electron. Imaging. 25(6), 061615 (Nov 22, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.061615


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