Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 1568-1575

  He, Jun ^a   Balzano, Laura ^b   Szlam, Arthur ^c  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND SEPARATION; COMPUTATION SPEED; COMPUTATION TIME; COMPUTER VISION PROBLEMS; FRAMES PER SECONDS; GRASSMANNIAN; LOW-DIMENSIONAL SUBSPACE; LOW-RANK MATRICES; NUMBER OF SAMPLES; ON-LINE ALGORITHMS; ROBUST ADAPTIVE; SEPARATION RATE; SUBSPACE ESTIMATION; SUBSPACE LEARNING;

ALGORITHMS; COMPUTER VISION; LAPTOP COMPUTERS; SEPARATION;

MATRIX ALGEBRA;

EID: 84866665003     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247848     Document Type: Conference Paper

References (28)

1. D. Babwin. Cameras make chicago most closely watched U.S. city. Huffington Post, April 6 2010. 1
2. L. Balzano, R. Nowak, and B. Recht. Online identification and tracking of subspaces from highly incomplete information. In Proceedings of Allerton, September 2010. Available at http://arxiv.org/abs/1006.4046. 1, 2, 4
3. L. Balzano, B. Recht, and R. Nowak. High-dimensional matched subspace detection when data are missing. In Proceedings of ISIT, 2010. 3
4. R. Basri and D. Jacobs. Lambertian reflectance and linear subspaces. IEEE TPAMI, 25(2):218-233, February 2003. 1, 2
5. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1-123, 2011. 3
6. E. Candès and B. Recht. Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6):717-772, 2009. 1
7. E. J. Candès, X. Li, Y. Ma, and J. Wright. Robust principal component analysis? Journal of the ACM, 58(1):1-37, 2009. 1, 2
8. V. Chandrasekaran, S. Sanghavi, P. Parrilo, and A. Willsky. Rank-sparsity incoherence for matrix decomposition. SIAM Journal on Optimization, 21:572, 2011. 2
9. J. P. Costeira and T. Kanade. A multibody factorization method for independently moving objects. International Journal of Computer Vision, 29, 1998. 1
10. A. Edelman, T. A. Arias, and S. T. Smith. The geometry of algorithms with orthogonality constraints. SIAM Journal on Matrix Analysis and Applications, 20(2):303-353, 1998. 3, 4
11. R. Epstein, P. Hallinan, and A. Yuille. 5+/-2 eigenimages suffice: An empirical investigation of low-dimensional lighting models. In PBMCV95, page SESSION 4, 1995. 2
12. P. J. Huber and E. M. Ronchetti. Robust Statistics. Wiley, 2009. 2
13. R. Keshavan, A. Montanari, and S. Oh. Matrix completion from noisy entries. Journal of Machine Learning Research, 11:2057-2078, July 2010. 1
14. S. Klein, J. Pluim, M. Staring, and M. Viergever. Adaptive stochastic gradient descent optimisation for image registration. International journal of computer vision, 81(3):227-239, 2009. 4
15. H. Krim and M. Viberg. Two decades of array signal processing research: the parametric approach. Signal Processing Magazine, IEEE, 13(4):67-94, July 1996. 1
16. L. Li,W. Huang, I. Y.-H. Gu, and Q. Tian. Statistical modeling of complex background for foreground object detection. IEEE Transactions on Image Processing, 13(11):1459-1472, November 2004. 1, 4, 5
17. Y. Li. On incremental and robust subspace learning. Pattern Recognition, 37(7):1509-1518, 2004. 2
18. Z. Lin, M. Chen, L.Wu, and Y. Ma. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Technical Report UILU-ENG-09-2215, UIUC, October 2009. Arxiv preprint arXiv:1009.5055. 4, 5
19. G. Mateos and G. B. Giannakis. Sparsity control for robust principal component analysis. In Proc. of Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2010. 2
20. M. McCahill and C. Norris. Cctv in london. Working Paper 6, Centre for Criminology and Criminal Justice, University of Hull, United Kingdom, June 2002. See http://www.urbaneye.net/results/ue-wp6.pdf. 1
21. N. Oliver, B. Rosario, and A. Pentland. A bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):831-843, 2000. 1, 2
22. C. Qiu and N. Vaswani. Reprocs: A missing link between recursive robust pca and recursive sparse recovery in large but correlated noise. Available at http://arxiv.org/abs/1106.3286, 2011. 2, 4, 5
23. B. Recht. A simpler approach to matrix completion. Journal of Machine Learning Research, 12:3413-3430, 2011. 1
24. R. Sivalingam, A. D'Souza, V. Morellas, N. Papanikolopoulos, M. Bazakos, and R. Miezianko. Dictionary learning for robust background modeling. In Proc. of the International Conference on Robotics and Automation (ICRA), 2011. 5
25. F. Torre and M. Black. A framework for robust subspace learning. Intl. J. of Computer Vision, 54:117142, 2003. 2
26. R. Vidal. Subspace clustering. IEEE Signal Processing Magazine, 2010. 1
27. L. Wang, L. Wang, M. Wen, Q. Zhuo, and W. Wang. Background subtraction using incremental subspace learning. In Proceedings of ICIP, 2007. 1
28. J. Wright, Y. Ma, A. Ganesh, and S. Rao. Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization. In Proceedings of NIPS, 2009. 2