Please use this identifier to cite or link to this item: https://doi.org/10.3934/ipi.2012.6.201
Title: Surveillance video processing using compressive sensing
Authors: Jiang, H.
Deng, W.
Shen, Z. 
Keywords: Alternating direction method
Background subtraction
Compressive sensing
Lowrank and sparse decomposition
Surveillance video
Tight frames
Issue Date: 2012
Citation: Jiang, H., Deng, W., Shen, Z. (2012). Surveillance video processing using compressive sensing. Inverse Problems and Imaging 6 (2) : 201-214. ScholarBank@NUS Repository. https://doi.org/10.3934/ipi.2012.6.201
Abstract: A compressive sensing method combined with decomposition of a matrix formed with image frames of a surveillance video into low rank and sparse matrices is proposed to segment the background and extract moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to reconstruct the video by a low rank and sparse decomposition of matrix. The low rank component represents the background, and the sparse component is used to identify moving objects in the surveillance video. The decomposition is performed by an augmented Lagrangian alternating direction method. Experiments are carried out to demonstrate that moving objects can be reliably extracted with a small amount of measurements. © 2012 American Institute of Mathematical Sciences.
Source Title: Inverse Problems and Imaging
URI: http://scholarbank.nus.edu.sg/handle/10635/104232
ISSN: 19308337
DOI: 10.3934/ipi.2012.6.201
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