May  2012, 6(2): 201-214. doi: 10.3934/ipi.2012.6.201

Surveillance video processing using compressive sensing

1. 

Bell Labs, Alcatel-Lucent, 700 Mountain Ave, Murray Hill, NJ 07974, United States

2. 

Dept. of Computational and Applied Math., Rice University, Houston, TX 77005, United States

3. 

Dept. of Math., National Univ. of Singapore, 119076, Singapore

Received  December 2011 Revised  February 2012 Published  May 2012

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.
Citation: Hong Jiang, Wei Deng, Zuowei Shen. Surveillance video processing using compressive sensing. Inverse Problems & Imaging, 2012, 6 (2) : 201-214. doi: 10.3934/ipi.2012.6.201
References:
[1]

Y. Benezeth, P. M. Jodoin, B. Emile, H. Laurent and C. Rosenberger, Comparative study of background subtraction algorithms,, J. Electron. Imaging, 19 (2010). doi: 10.1117/1.3456695.

[2]

J.-F. Cai, E. J. Candès and Z. Shen, A singular value thresholding algorithm for matrix completion,, SIAM Journal on Optimization, 20 (2010), 1956. doi: 10.1137/080738970.

[3]

J.-F. Cai, S. Osher and Z. Shen, Split Bregman methods and frame based image restoration,, Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 8 (): 337.

[4]

E.-J. Candès, X. Li, Y. Ma and J. Wright, Robust principal component analysis?,, Journal of ACM, 58 (2011).

[5]

E. J. Candès, J. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements,, Comm. Pure Appl. Math., 59 (2006), 1207. doi: 10.1002/cpa.20124.

[6]

V. Cevher, A. Sankaranarayanan, M. Duarte, D. Reddy, R. Baraniuk and R. Chellappa, Compressive sensing for background subtraction,, Computer Vision-ECCV 2008, (2008), 155.

[7]

I. Daubechies, B. Han, A. Ron and Z. Shen, Framelets: MRA-based constructions of wavelet frames,, Applied and Computational Harmonic Analysis, 14 (2003), 1. doi: 10.1016/S1063-5203(02)00511-0.

[8]

W. Deng, W. Yin and Y. Zhang, "Group Sparse Optimization by Alternating Direction Method,", TR11-06, (2011), 11.

[9]

B. Dong and Z. Shen, "MRA-Based Wavelet Frames and Applications,", IAS Lecture Notes Series, (2010).

[10]

Y. Dong, G. N. DeSouza and T. X. Han, Illumination invariant foreground detection using multi-subspace learning,, International Journal of Knowledge-based and Intelligent Engineering Systems, 14 (2010), 31.

[11]

D. Donoho, Compressed sensing,, IEEE Trans. on Information Theory, 52 (2006), 1289. doi: 10.1109/TIT.2006.871582.

[12]

M. Fornasier and H. Rauhut, Recovery algorithms for vector-valued data with joint sparsity constraints,, SIAM J. Numer. Anal., 46 (2008), 577. doi: 10.1137/0606668909.

[13]

H. Gao, J. F. Cai, Z. Shen and H. Zhao, Robust principal component analysis-based four-dimensional computed tomography,, Physics in Medicine and Biology, 56 (2011). doi: 10.1088/0031-9155/56/11/002.

[14]

R. Glowinski and P. Le Tallec, "Augmented Lagrangian and Operator Splitting Methods in Nonlinear Mechanics,", SIAM Studies in Applied Mathematics, 9 (1989). doi: 10.1137/1.9781611970838.

[15]

T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems,, SIAM J. Imaging Sci., 2 (2009), 323. doi: 10.1137/080725891.

[16]

H. Jiang, Chengbo Li, Raziel Haimi-Cohen, Paul Wilford and Yin Zhang, Scalable video coding using compressive sensing,, Bell Labs Technical Journal, 16 (2012).

[17]

H. Jiang, B. Mathews and P. Wilford, Compressive sensing for sound localization in wireless sensor network,, accepted for presentation at SENSORNET2012, (2012), 24.

[18]

H. Jiang, Z. Shen, W. Deng and P. Wilford, Adaptive low rank and sparse decomposition in compressive sensing of surveillance video,, submitted, (2011).

[19]

C. Li, H. Jiang, P. A., Wilford and Y. Zhang, Video coding using compressive sensing for wireless communications,, IEEE Wireless Communications and Networking Conference (WCNC), (2011), 2077. doi: 10.1109/WCNC.2011.5779474.

[20]

V. Mahadevan and N. Vasconcelos, Spatiotemporal saliency in highly dynamic scenes,, IEEE Trans. on Pattern Analysis and Machine Intelligence, 32 (2010), 171. doi: 10.1109/TPAMI.2009.112.

[21]

M. Piccardi, Background subtraction techniques: A review,, IEEE International Conference on Systems, 4 (2004), 3099.

[22]

A. Ron and Z. Shen, Affine systems in $L_2(R^d)$: The analysis of the analysis operator,, Journal of Functional Analysis, 148 (1997), 408. doi: 10.1006/jfan.1996.3079.

[23]

M. Rudelson and R. Vershynin, On sparse reconstruction from Fourier and Gaussian measurements,, Communications on Pure and Applied Mathematics, 61 (2008), 1025. doi: 10.1002/cpa.20227.

[24]

Z. Shen, Wavelet frames and image restorations,, Proceedings of the International Congress of Mathematicians, IV (2010), 2834.

[25]

C. Stauffer and W. E. L Grimson, Adaptive background mixture models for real-time tracking,, Computer Vision and Pattern Recognition, 2 (1999), 252.

[26]

E. Sutter, The Fast $m$-Transform: A fast computation of cross-correlations with binary $m$-sequences,, SIAM J. Comput., 20 (1991), 686. doi: 10.1137/0220043.

[27]

, EC Funded CAVIAR project/IST 2001 37540,, 2003. Available from: \url{http://homepages.inf.ed.ac.uk/rbf/CAVIAR/}., ().

show all references

References:
[1]

Y. Benezeth, P. M. Jodoin, B. Emile, H. Laurent and C. Rosenberger, Comparative study of background subtraction algorithms,, J. Electron. Imaging, 19 (2010). doi: 10.1117/1.3456695.

[2]

J.-F. Cai, E. J. Candès and Z. Shen, A singular value thresholding algorithm for matrix completion,, SIAM Journal on Optimization, 20 (2010), 1956. doi: 10.1137/080738970.

[3]

J.-F. Cai, S. Osher and Z. Shen, Split Bregman methods and frame based image restoration,, Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 8 (): 337.

[4]

E.-J. Candès, X. Li, Y. Ma and J. Wright, Robust principal component analysis?,, Journal of ACM, 58 (2011).

[5]

E. J. Candès, J. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements,, Comm. Pure Appl. Math., 59 (2006), 1207. doi: 10.1002/cpa.20124.

[6]

V. Cevher, A. Sankaranarayanan, M. Duarte, D. Reddy, R. Baraniuk and R. Chellappa, Compressive sensing for background subtraction,, Computer Vision-ECCV 2008, (2008), 155.

[7]

I. Daubechies, B. Han, A. Ron and Z. Shen, Framelets: MRA-based constructions of wavelet frames,, Applied and Computational Harmonic Analysis, 14 (2003), 1. doi: 10.1016/S1063-5203(02)00511-0.

[8]

W. Deng, W. Yin and Y. Zhang, "Group Sparse Optimization by Alternating Direction Method,", TR11-06, (2011), 11.

[9]

B. Dong and Z. Shen, "MRA-Based Wavelet Frames and Applications,", IAS Lecture Notes Series, (2010).

[10]

Y. Dong, G. N. DeSouza and T. X. Han, Illumination invariant foreground detection using multi-subspace learning,, International Journal of Knowledge-based and Intelligent Engineering Systems, 14 (2010), 31.

[11]

D. Donoho, Compressed sensing,, IEEE Trans. on Information Theory, 52 (2006), 1289. doi: 10.1109/TIT.2006.871582.

[12]

M. Fornasier and H. Rauhut, Recovery algorithms for vector-valued data with joint sparsity constraints,, SIAM J. Numer. Anal., 46 (2008), 577. doi: 10.1137/0606668909.

[13]

H. Gao, J. F. Cai, Z. Shen and H. Zhao, Robust principal component analysis-based four-dimensional computed tomography,, Physics in Medicine and Biology, 56 (2011). doi: 10.1088/0031-9155/56/11/002.

[14]

R. Glowinski and P. Le Tallec, "Augmented Lagrangian and Operator Splitting Methods in Nonlinear Mechanics,", SIAM Studies in Applied Mathematics, 9 (1989). doi: 10.1137/1.9781611970838.

[15]

T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems,, SIAM J. Imaging Sci., 2 (2009), 323. doi: 10.1137/080725891.

[16]

H. Jiang, Chengbo Li, Raziel Haimi-Cohen, Paul Wilford and Yin Zhang, Scalable video coding using compressive sensing,, Bell Labs Technical Journal, 16 (2012).

[17]

H. Jiang, B. Mathews and P. Wilford, Compressive sensing for sound localization in wireless sensor network,, accepted for presentation at SENSORNET2012, (2012), 24.

[18]

H. Jiang, Z. Shen, W. Deng and P. Wilford, Adaptive low rank and sparse decomposition in compressive sensing of surveillance video,, submitted, (2011).

[19]

C. Li, H. Jiang, P. A., Wilford and Y. Zhang, Video coding using compressive sensing for wireless communications,, IEEE Wireless Communications and Networking Conference (WCNC), (2011), 2077. doi: 10.1109/WCNC.2011.5779474.

[20]

V. Mahadevan and N. Vasconcelos, Spatiotemporal saliency in highly dynamic scenes,, IEEE Trans. on Pattern Analysis and Machine Intelligence, 32 (2010), 171. doi: 10.1109/TPAMI.2009.112.

[21]

M. Piccardi, Background subtraction techniques: A review,, IEEE International Conference on Systems, 4 (2004), 3099.

[22]

A. Ron and Z. Shen, Affine systems in $L_2(R^d)$: The analysis of the analysis operator,, Journal of Functional Analysis, 148 (1997), 408. doi: 10.1006/jfan.1996.3079.

[23]

M. Rudelson and R. Vershynin, On sparse reconstruction from Fourier and Gaussian measurements,, Communications on Pure and Applied Mathematics, 61 (2008), 1025. doi: 10.1002/cpa.20227.

[24]

Z. Shen, Wavelet frames and image restorations,, Proceedings of the International Congress of Mathematicians, IV (2010), 2834.

[25]

C. Stauffer and W. E. L Grimson, Adaptive background mixture models for real-time tracking,, Computer Vision and Pattern Recognition, 2 (1999), 252.

[26]

E. Sutter, The Fast $m$-Transform: A fast computation of cross-correlations with binary $m$-sequences,, SIAM J. Comput., 20 (1991), 686. doi: 10.1137/0220043.

[27]

, EC Funded CAVIAR project/IST 2001 37540,, 2003. Available from: \url{http://homepages.inf.ed.ac.uk/rbf/CAVIAR/}., ().

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