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The effect of recovery algorithms on compressive sensing background subtraction

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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The effect of recovery algorithms on compressive sensing background subtraction. / Davies, Rhian; Mihaylova, Lyudmila; Pavlidis, Nicos et al.
Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2013 Workshop on. IEEE, 2013. p. 1-6.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Davies, R, Mihaylova, L, Pavlidis, N & Eckley, I 2013, The effect of recovery algorithms on compressive sensing background subtraction. in Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2013 Workshop on. IEEE, pp. 1-6. https://doi.org/10.1109/SDF.2013.6698258

APA

Davies, R., Mihaylova, L., Pavlidis, N., & Eckley, I. (2013). The effect of recovery algorithms on compressive sensing background subtraction. In Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2013 Workshop on (pp. 1-6). IEEE. https://doi.org/10.1109/SDF.2013.6698258

Vancouver

Davies R, Mihaylova L, Pavlidis N, Eckley I. The effect of recovery algorithms on compressive sensing background subtraction. In Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2013 Workshop on. IEEE. 2013. p. 1-6 doi: 10.1109/SDF.2013.6698258

Author

Davies, Rhian ; Mihaylova, Lyudmila ; Pavlidis, Nicos et al. / The effect of recovery algorithms on compressive sensing background subtraction. Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2013 Workshop on. IEEE, 2013. pp. 1-6

Bibtex

@inproceedings{60603194686d4ec59504951cb55ebee4,
title = "The effect of recovery algorithms on compressive sensing background subtraction",
abstract = "Background subtraction is a key method required to aid processing surveillance videos. Current methods require storing each pixel of every video frame, which can be wasteful as most of this information refers to the uninteresting background. Compressive sensing can offer an efficient solution by using the fact that foreground is often sparse in the spatial domain. By making this assumption and applying a specific recovery algorithm to a trained background, it is possible to reconstruct the foreground, using only a low dimensional representation of the difference between the current frame and the estimated background scene. Although new compressive sensing background subtraction algorithms are being created, no study has been made of the effect of recovery algorithms on performance of background subtraction. This is considered by applying both Basis Pursuit and Orthogonal Matching Pursuit (OMP) to a standard test video, and comparing their accuracy.",
author = "Rhian Davies and Lyudmila Mihaylova and Nicos Pavlidis and Idris Eckley",
year = "2013",
month = oct,
doi = "10.1109/SDF.2013.6698258",
language = "English",
isbn = "9781479907779",
pages = "1--6",
booktitle = "Sensor Data Fusion",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - The effect of recovery algorithms on compressive sensing background subtraction

AU - Davies, Rhian

AU - Mihaylova, Lyudmila

AU - Pavlidis, Nicos

AU - Eckley, Idris

PY - 2013/10

Y1 - 2013/10

N2 - Background subtraction is a key method required to aid processing surveillance videos. Current methods require storing each pixel of every video frame, which can be wasteful as most of this information refers to the uninteresting background. Compressive sensing can offer an efficient solution by using the fact that foreground is often sparse in the spatial domain. By making this assumption and applying a specific recovery algorithm to a trained background, it is possible to reconstruct the foreground, using only a low dimensional representation of the difference between the current frame and the estimated background scene. Although new compressive sensing background subtraction algorithms are being created, no study has been made of the effect of recovery algorithms on performance of background subtraction. This is considered by applying both Basis Pursuit and Orthogonal Matching Pursuit (OMP) to a standard test video, and comparing their accuracy.

AB - Background subtraction is a key method required to aid processing surveillance videos. Current methods require storing each pixel of every video frame, which can be wasteful as most of this information refers to the uninteresting background. Compressive sensing can offer an efficient solution by using the fact that foreground is often sparse in the spatial domain. By making this assumption and applying a specific recovery algorithm to a trained background, it is possible to reconstruct the foreground, using only a low dimensional representation of the difference between the current frame and the estimated background scene. Although new compressive sensing background subtraction algorithms are being created, no study has been made of the effect of recovery algorithms on performance of background subtraction. This is considered by applying both Basis Pursuit and Orthogonal Matching Pursuit (OMP) to a standard test video, and comparing their accuracy.

U2 - 10.1109/SDF.2013.6698258

DO - 10.1109/SDF.2013.6698258

M3 - Conference contribution/Paper

SN - 9781479907779

SP - 1

EP - 6

BT - Sensor Data Fusion

PB - IEEE

ER -