Real-time principal component pursuit

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn , Issue , 2011, Pages 1433-1437

  Pope, Graeme ^a   Baumann, Manuel ^a   Studer, Christoph ^b   Durisi, Giuseppe ^c  

^a ETH ZURICH   (Switzerland)

^b Rice University   (United States)

^c CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR VIDEO; FOREGROUND/BACKGROUND; FRAMES PER SECONDS; LOW-RANK MATRICES; PRINCIPAL COMPONENTS; REALTIME PROCESSING; ROBUST PRINCIPAL COMPONENT ANALYSIS; SPARSE MATRICES; SYNTHETIC DATA; SYSTEMATIC ANALYSIS; VIDEO SURVEILLANCE;

COMPUTATIONAL COMPLEXITY; CONVEX OPTIMIZATION; FACE RECOGNITION; SECURITY SYSTEMS; VIDEO STREAMING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84861303371     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2011.6190254     Document Type: Conference Paper

References (16)

1. I. T. Jolliffe, Principal Component Analysis, 2nd ed., ser. Springer Series in Statistics. New York, NY, U.S.A.: Springer, 2002.
2. Z. Lin, M. Chen, L. Wu, and Y. Ma, "The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices,"Oct. 2009. [Online]. Available: http://arxiv.org/abs/1009.5055
3. E. J. Candès, X. Li, Y. Ma, and J. Wright, "Robust principal component analysis?" Jan 2009. [Online]. Available: http://arxiv.org/abs/ 0912.3599v1
4. V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, "Ranksparsity incoherence for matrix decomposition," SIAM J. Opt., vol. 21, no. 2, pp. 572-596, 2011.
5. X. Yuan and J. Yang, "Sparse and low-rank matrix decomposition via alternating direction methods," preprint, 2009.
6. M. Grant and S. Boyd, "CVX: Matlab software for disciplined convex programming." [Online]. Available: http://cvxr.com/cvx/
7. D. P. Bertsekas, Constrained Optimisation and Lagrange Multiplier Methods. Belmont, MA, U.S.A.: Athena Scientific, 1982.
8. C. Qiu and N. Vaswani, "Real-time robust principal components' pursuit,"in Proc. Allerton Conf. Commun., Contr., Comput., Monticello, IL, U.S.A., Oct. 2010, pp. 591-598.
9. A. Bruckstein, D. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images," SIAM Review, vol. 51, no. 1, pp. 34-81, 2009.
10. E. J. Candès and B. Recht, "Exact matrix completion via convex optimization," Found. Comput. Math., vol. 9, no. 6, pp. 717-772, Apr. 2009.
11. J. C. Bezdek and R. J. Hathaway, "Some notes on alternating optimization,"Lecture Notes in Computer Science, vol. 2275, pp. 187-195, 2002.
12. S. Shlien, "A method for computing the partial singular value decomposition,"IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-4, no. 6, pp. 671-676, Jun. 1982.
13. J. C. Nash, "A one-sided transformation method for the singular value decomposition and algebraic eigenproblem," Computer Journal, vol. 18, no. 1, pp. 74-76, Jan. 1975.
14. J. C. Nash and S. Shlien, "Simple algorithms for the partial singular value decomposition," Computer Journal, vol. 30, no. 3, pp. 268-275, Jan. 1987.
15. E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen, LAPACK Users' Guide, 3rd ed. Philadelphia, PA, U.S.A: Society for Industrial and Applied Mathematics, 1999.
16. L. Li, W. Huang, I. Y.-H. Gu, and Q. Tian, "Statistical modeling of complex backgrounds for foreground object detection," IEEE Trans. Image Process, vol. 13, no. 11, pp. 1459-1472, Nov. 2004.