Compressive Sensing

Basis Pursuit Denoising (BPDN)

V. Cevher, D. Reddy, M. Duarte, A. Sankaranarayanan, R. Chellappa, R. Baraniuk, “Compressive Sensing for Background Subtraction”, European Conference on Computer Vision, ECCV 2008, October 2008.

Lattice Matching Pursuit (LaMP)

V. Cevher, M. Duarte, C. Hedge, R. Baraniuk, “Sparse Signal Recovery Using Markov Random Fields”, NIPS 2008, 2008.

l1-l1 Minimization

J. Mota, N. Deligiannis, A. Sankaranarayanan, V. Cevher, M. Rodrigues, "Dynamic Sparse State Estimation using l1-l1 Minimization: Adaptive-Rate Measurement Bounds, Algorithms and Applications", International Conference on Acoustics, Speech and Signal Processing ICASSP 2015, April 2015.

J. Mota, N. Deligiannis, A. Sankaranarayanan, V. Cevher, M. Rodrigues “Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction”, Preprint, March 2015.

Compressive Sampling Matching Pursuit (CoSaMP)

D. Needell, J. Tropp, “CoSaMP: Iterative signal recovery from incomplete and inaccurate samples”, Applied and Computational Harmonic Analysis, June 2008.

Compressive Sampling Matching Pursuit (CoSaMP_subspace)

J. He, M. Gao, L. Zhang, H. Wu, “Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement”, Algorithms 2013, Volume 6, Issue 4, pages 871-882, 2013.

Adaptive Rate Compressive Sensing (ARCS)

G. Warnell, D. Reddy, R. Chellappa, “Adaptive Rate Compressive Sensing for Background Subtraction”, IEEE International Conference on Acoustics, Speech, and Signal Processing, Kyoto, Japan. March 2012

Adaptive Rate Compressive Sensing via Cross Validation (ARCS-CV)

G. Warnell, S. Bhattacharya, R. Chellappa, T. Basar, “Adaptive-Rate Compressive Sensing via Side Information, January 2014.

Running Average

J. Li, J. Wang, W. Shen, “Moving object detection in framework of compressive sampling”, Journal of Systems Engineering and Electronics, Volume 21, No. 5, pages 740–745, October 2010.

Gradient Projection for Sparse Reconstruction (GPSR)

X. Wang, F. Liu, Z. Ye, “Background Modeling in Compressed Sensing Scheme”, ESEP 2011, pages 4776-4783, December 2011.

K-cluster

M. Xu, J. Lu, “K-cluster-valued compressive sensing for imaging”, EURASIP Journal on Advances in Signal Processing, 2011.

Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP)

R. Davies, L. Mihaylova, N. Pavlidis, I. Eckley, “The effect of recovery algorithms on compressive sensing background subtraction”, Workshop Sensor Data Fusion: Trends, Solutions, and Applications, 2013.

Sparse Representation (SR)

Y. Wang, Q. Lu, D. Wang, W. Liu, “Compressive Background Modeling for Foreground Extraction”, Journal of Electrical and Computer Engineering, Hindawi Publishing Corporation, March 2015.

Convex Lattice Matching Pursuit (CoLaMP)

S. Shah, T. Goldstein, C. Studer, “Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity”, Preprint, 2016.

CS-MoG

Y. Shen, W. Hu, M. Yang, J. Liu, B. Wei, S. Lucey, C. Chou, “Real-time and Robust Compressive Background Subtraction for Embedded Camera Networks”, IEEE Transactions on Mobile Computing, 2016.

L1-PCA

Y. Liu, D. Pados, “Compressed-Sensed-Domain L1-PCA Video Surveillance”, IEEE Transactions on Multimedia, 2016.