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