Surveillance video processing using compressive sensing

Hong Jiang; Wei Deng; Zuowei Shen

doi:10.3934/ipi.2012.6.201

Article Contents

2012, Volume 6, Issue 2: 201-214. Doi: 10.3934/ipi.2012.6.201

This issue Previous Article Besov priors for Bayesian inverse problems Next Article Non-Gaussian statistical inverse problems. Part I: Posterior distributions

Surveillance video processing using compressive sensing

1.
Bell Labs, Alcatel-Lucent, 700 Mountain Ave, Murray Hill, NJ 07974
2.
Dept. of Computational and Applied Math., Rice University, Houston, TX 77005
3.
Dept. of Math., National Univ. of Singapore, 119076

Received: November 30, 2011

Revised: January 31, 2012

Published: April 2012

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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.

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Mathematics Subject Classification: Primary: 00A69, 41-02; Secondary: 46N10.

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