Robust background subtraction via online robust PCA using image decomposition

Javed, Sajid; Oh, Seon Ho; Heo, Jun-Hyeok; Jung, Soon Ki

doi:10.1145/2663761.2664195

Cited by 21 publications

(18 citation statements)

References 12 publications

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“…The results are encouraging for background subtraction, but no theoretic guarantee of the algorithm convergence for GRASTA is provided. Therefore, Javed et al 10,21 modified OR-PCA for background/foreground separation. Real-time processing is achieved, but it does not require learning rate tuning as do regular stochastic gradient descents.…”

Section: Related Workmentioning

confidence: 99%

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

Javed

Bouwmans

et al. 2015

J. Electron. Imaging

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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, highdimensional 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. Javed et al.: Robust background subtraction to global illumination changes via multiple features-based. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/18/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Section: Related Workmentioning

confidence: 99%

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

Javed

Bouwmans

et al. 2015

J. Electron. Imaging

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“…Finally, the foreground detection is performed on the results of OR-PCA. Multiple feature integration into OR-PCA improves the quality of foreground and increases the quantitative performance as compared to other RPCA via PCP based methods [1] and single feature OR-PCA [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, it becomes really hard task when the scene has sudden illumination change or geometrical changes such as waving trees, water surfaces, etc. [6] Many algorithms have been developed to tackle the challenging problems in the background subtraction (also known as foreground detection) [6], [5]. Among them, Robust Principal Component Analysis (RPCA) based approach shows a very nice framework for separating foreground objects from highly dynamic background scenes.…”

Section: Introductionmentioning

confidence: 99%

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OR-PCA with dynamic feature selection for robust background subtraction

Javed

Sobral

Bouwmans

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Background modeling and foreground object detection is the first step in visual surveillance system. The task becomes more difficult when the background scene contains significant variations, such as water surface, waving trees and sudden illumination conditions, etc. Recently, subspace learning model such as Robust Principal Component Analysis (RPCA) provides a very nice framework for separating the moving objects from the stationary scenes. However, due to its batch optimization process, high dimensional data should be processed. As a result, huge computational complexity and memory problems occur in traditional RPCA based approaches. In contrast, Online Robust PCA (OR-PCA) has the ability to process such large dimensional data via stochastic manners. OR-PCA processes one frame per time instance and updates the subspace basis accordingly when a new frame arrives. However, due to the lack of features, the sparse component of OR-PCA is not always robust to handle various background modeling challenges. As a consequence, the system shows a very weak performance, which is not desirable for real applications. To handle these challenges, this paper presents a multi-feature based OR-PCA scheme. A multi-feature model is able to build a robust low-rank background model of the scene. In addition, a very nice feature selection process is designed to dynamically select a useful set of features frame by frame, according * Prof. Jung is a corresponding author.to the weighted sum of total features. Experimental results on challenging datasets such as Wallflower, I2R and BMC 2012 show that the proposed scheme outperforms the state of the art approaches for the background subtraction task.

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“…Many algorithms have been proposed to handle the problem of background subtraction [15] and several implementations are available in BGS 67 and LRS 68 libraries. Excellent surveys on background modeling and foreground detection can be found in [10].…”

Section: Introductionmentioning

confidence: 99%