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Article

Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow

1
Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, 91058 Erlangen, Germany
2
Human Machine Interaction, University of Engineering and Technology, Vietnam National University, Hanoi 100000, Vietnam
*
Author to whom correspondence should be addressed.
Current address: Digital Cinema Group, Fraunhofer IIS, 91058 Erlangen, Germany.
Current address: Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
J. Imaging 2018, 4(7), 90; https://doi.org/10.3390/jimaging4070090
Received: 1 May 2018 / Revised: 15 June 2018 / Accepted: 27 June 2018 / Published: 3 July 2018
(This article belongs to the Special Issue Detection of Moving Objects)
In the context of video background–foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-1 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background–foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods. View Full-Text
Keywords: robust principal component analysis; video separation; compressive measurements; prior information; optical flow; motion estimation; motion compensation robust principal component analysis; video separation; compressive measurements; prior information; optical flow; motion estimation; motion compensation
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MDPI and ACS Style

Prativadibhayankaram, S.; Luong, H.V.; Le, T.H.; Kaup, A. Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow. J. Imaging 2018, 4, 90. https://doi.org/10.3390/jimaging4070090

AMA Style

Prativadibhayankaram S, Luong HV, Le TH, Kaup A. Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow. Journal of Imaging. 2018; 4(7):90. https://doi.org/10.3390/jimaging4070090

Chicago/Turabian Style

Prativadibhayankaram, Srivatsa; Luong, Huynh V.; Le, Thanh H.; Kaup, André. 2018. "Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow" J. Imaging 4, no. 7: 90. https://doi.org/10.3390/jimaging4070090

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