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Robust background subtraction to global illumination changes via multiple features-based online robust principal components analysis with Markov random field

[+] Author Affiliations
Sajid Javed, Seon Ho Oh, Soon Ki Jung

Kyungpook National University, School of Computer Science and Engineering, 80 Daehak-ro, Buk-gu, Daegu 702-701, Republic of Korea

Thierry Bouwmans

Université de La Rochelle, Laboratoire Mathematiques, Image et Applications, 23 Avenue Albert Einstein, La Rochelle, BP 33060-17301, France

J. Electron. Imaging. 24(4), 043011 (Aug 11, 2015). doi:10.1117/1.JEI.24.4.043011
History: Received December 31, 2014; Accepted July 8, 2015
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Abstract.  Background subtraction is an important task for various computer vision applications. The task becomes more critical when the background scene contains more variations, such as swaying trees and abruptly changing lighting conditions. Recently, robust principal component analysis (RPCA) has been shown to be a very efficient framework for moving-object detection. However, due to its batch optimization process, high-dimensional data need to be processed. As a result, computational complexity, lack of features, weak performance, real-time processing, and memory issues arise in traditional RPCA-based approaches. To handle these, a background subtraction algorithm robust against global illumination changes via online robust PCA (OR-PCA) using multiple features together with continuous constraints, such as Markov random field (MRF), is presented. OR-PCA with automatic parameter estimation using multiple features improves the background subtraction accuracy and computation time, making it attractive for real-time systems. Moreover, the application of MRF to the foreground mask exploits structural information to improve the segmentation results. In addition, global illumination changes in scenes are tackled by using sum of the difference of similarity measure among features, followed by a parameter update process using a low-rank, multiple features model. Evaluation using challenging datasets demonstrated that the proposed scheme is a top performer for a wide range of complex background scenes.

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Citation

Sajid Javed ; Seon Ho Oh ; Thierry Bouwmans and Soon Ki Jung
"Robust background subtraction to global illumination changes via multiple features-based online robust principal components analysis with Markov random field", J. Electron. Imaging. 24(4), 043011 (Aug 11, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.4.043011


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