Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computer Vision and Pattern Recognition

arXiv:1302.1942 (cs)
[Submitted on 8 Feb 2013]

Title:Surveillance Video Processing Using Compressive Sensing

Authors:Hong Jiang, Wei Deng, Zuowei Shen
View PDF
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.
Comments: 14 pages, 5 figures
Subjects: Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT)
MSC classes: Primary: 00A69, 41-02, Secondary: 46N10
Cite as: arXiv:1302.1942 [cs.CV]
  (or arXiv:1302.1942v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1302.1942
Journal reference: Inverse Problems and Imaging, Volume 6, No. 2, 2012, 201-214
Related DOI: https://doi.org/10.3934/ipi.2012.6.201

Submission history

From: Hong Jiang [view email]
[v1] Fri, 8 Feb 2013 04:47:22 UTC (529 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.CV
< prev   |   next >
new | recent | 2013-02
Change to browse by:
cs
cs.IT
math
math.IT

References & Citations

DBLP - CS Bibliography

listing | bibtex
Hong Jiang
Wei Deng
Zuowei Shen
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)