Foreground estimation based on linear regression model with fused sparsity on outliers

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 23, Issue 8, 2013, Pages 1346-1357

  Xue, Gengjian ^a,b   Song, Li ^a,b   Sun, Jun ^a,b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Shanghai Key Labs of Digital Media Processing and Communication   (China)

Author keywords

Foreground detection; fused sparsity constraint; outlier estimation; robust linear regression model

Indexed keywords

COMPUTER VISION APPLICATIONS; FOREGROUND DETECTION; LINEAR REGRESSION MODELS; OPTIMIZED SOLUTIONS; ROBUST LINEAR REGRESSION; SIGNAL ESTIMATION; SPARSITY CONSTRAINTS; STATE-OF-THE-ART TECHNIQUES;

SIGNAL ANALYSIS; STATISTICS;

LINEAR REGRESSION;

EID: 84881440163     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2013.2243053     Document Type: Article

References (41)

1. C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, "Pfinder: Real-time tracking of the human body," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 780-785, Jul. 1997. (Pubitemid 127762271)
2. C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 2. Jun. 1999, pp. 246-252.
3. Z. Zivkovic and F. van der Heijden, "Efficient adaptive density estimation per image pixel for the task of background subtraction," Pattern Recognit. Lett., vol. 27, no. 7, pp. 773-780, 2006. (Pubitemid 43374860)
4. D. S. Lee, "Effective Gaussian mixture learning for video background subtraction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 5, pp. 827-832, May 2005.
5. H. H. Lin, J. H. Chuang, and T. L. Liu, "Regularized background adaptation: A novel learning rate control scheme for Gaussian mixture modeling," IEEE Trans. Image Process., vol. 20, no. 3, pp. 822-836, Mar. 2011.
6. T. Bouwmans, F. E. Baf, and B. Vachon, "Background modeling using mixture of Gaussians for foreground detection-a survey," Recent Patents Comput. Sci., vol. 1, no. 3, pp. 219-237, Nov. 2008.
7. A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, "Background and foreground modeling using nonparametric kernel density estimation for visual surveillance," Proc. IEEE, vol. 90, no. 7, pp. 1151-1163, Jul. 2002.
8. A. Mittal and N. Paragios, "Motion-based background subtraction using adaptive kernel density estimation," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 2. Jun.-Jul. 2004, pp. 302-309.
9. R. Vemulapalli and R. Aravind, "Spatio-temporal nonparametric background modeling and subtraction," in Proc. IEEE Int. Conf. Comput. Vision Workshops, Sep.-Oct. 2009, pp. 1145-1152.
10. Y. Sheikh and M. Shah, "Bayesian modeling of dyanmic scenes for object detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 11, pp. 1778-1792, Nov. 2005. (Pubitemid 41685113)
11. T. Bouwmans, F. E. Baf, and B. Vachon, Statistical Background Modeling for Foreground Detection: A Survey. Singapore: World Scientific Publishing, 2010, vol. 4, ch. 3, pp. 181-199.
12. M. Heikkilä and M. Pietikäinen, "A texture-based method for modelling the background and detecting moving objects," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 4, pp. 657-662, Apr. 2006. (Pubitemid 43264663)
13. S. Liao, G. Zhao, V. Kellokumpu, M. Pietikäinen, and S. Z. Li, "Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 2. Jun. 2010, pp. 1301-1306.
14. B. Zhang, Y. Gao, S. Zhao, and B. Zhong, "Kernel similarity modeling of texture pattern flow for motion detection in complex background," IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 1, pp. 29-38, Jan. 2011.
15. N. M. Oliver, B. Rosario, and A. P. Pentland, "A Bayesian computer vision system for modeling human interactions," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 831-843, Aug. 2000.
16. L. Wang, L. Wang, M. Wen, Q. Zhuo, and W. Wang, "Background subtraction using incremental subspace learning," in Proc. IEEE Int. Conf. Image Processing, vol. 5. Sep.-Oct. 2007, pp. 45-48.
17. D. M. Tsai and S. C. Lai, "Independent component analysis-based background subtraction for indoor surveillance," IEEE Trans. Image Process., vol. 18, no. 1, pp. 158-167, Jan. 2009.
18. J. Wright, A. Ganesh, S. Rao, Y. Peng, and Y. Ma, "Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization," in Proc. Neural Inform. Process. Syst., pp. 2080-2088, Dec. 2009.
19. Z. Zhou, X. Li, J. Wright, E. Candès, and Y. Ma, "Stable principal component pursuit," in Proc. IEEE Int. Symp. Inform. Theory, Jan. 2010, pp. 1518-1522.
20. T. Zhou and D. Tao, "Godec: Randomized low-rank & sparse matrix decomposition in noisy case," in Proc. 28th Int. Conf. Mach. Learning, 2011, pp. 33-40.
21. P. J. Huber and E. M. Ronchetti, Robust Statistics, 2nd ed. New Jersey, USA: Wiley, 2009.
22. R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Roy. Statist. Soc. Ser. B, vol. 58, no. 1, pp. 267-288, 1996.
23. M. Yuan and Y. Lin, "Model selection and estimation in regression with grouped variables," J. Roy. Statist. Soc. Ser. B, vol. 68, no. 1, pp. 49-67, 2006. (Pubitemid 43415335)
24. R. Tibshirani, "Regression shrinkage and selection via the Lasso: A retrospective," J. Roy. Statist. Soc. Ser. B, vol. 73, no. 3, pp. 273-282, 2011.
25. M. Dikmen and T. Huang, "Robust estimation of foreground in surveillance videos by sparse error estimation," in Proc. IEEE Int. Conf. Pattern Recognit., Dec. 2008, pp. 1-4.
26. M. Dikmen, S.-F. Tsai, and T. Huang, "Base selection in estimating sparse foreground in video," in Proc. IEEE Int. Conf. Image Process., Nov. 2009, pp. 3217-3220.
27. J. Huang, X. Huang, and D. Metaxas, "Learning with dynamic group sparsity," in Proc. IEEE Int. Conf. Comput. Vision, Sep.-Oct. 2009, pp. 64-71.
28. G. Xue, L. Song, J. Sun, and M. Wu, "Foreground estimation based on robust linear regression model," in Proc. IEEE Int. Conf. Image Process., Sep. 2011, pp. 3269-3272.
29. R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, "Sparsity and smoothness via the Fused Lasso," J. Roy. Statist. Soc. Ser. B, vol. 67, no. 1, pp. 91-108, 2005. (Pubitemid 40167043)
30. D. Goldfarb, S. Ma, and K. Scheinberg, "Fast alternating linearization methods for minimizing the sum of two convex functions," Math. Programming Series A., pp. 1-34, Mar. 2012.
31. E. Candès and T. Tao, "Decoding by linear programming," IEEE Trans. Inform. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005. (Pubitemid 41800353)
32. J. Liu, S. Ji, and J. Ye, "SLEP: Sparse learning with efficient projections," Arizona State University, 2009 [Onine] Available: http://www.public.asu.edu/~jye02/Software/SLEP/index.htm.
33. G. Schwarz, "Estimating the dimension of a model," Ann. Statist., vol. 6, no. 2, pp. 461-464, 1978.
34. H. Akaike, "A new look at the statistical model identification, " IEEE Trans. Autom. Control, vol. 19, no. 6, pp. 716-723, Dec. 1974.
35. J. Rissanen, "Modeling by shortest data description," Automatica, vol. 14, no. 5, pp. 465-471, 1978.
36. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Rev., vol. 43, no. 1, pp. 129-159, 2001. (Pubitemid 32406896)
37. P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection. New York, USA: Wiley, 1987.
38. J. Friedman, T. Hastie, H. Hofling, and R. Tibshirani, "Pathwise coordinate optimization," Ann. Appl. Stat., vol. 1, no. 2, pp. 302-332, 2007.
39. 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.
40. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and practice of background maintenance," in Proc. IEEE Int. Conf. Comput. Vision, Sep. 1999, pp. 255-261. (Pubitemid 32080048)
41. G. Xue, J. Sun, and L. Song, "Background subtraction based on phase feature and distance transform," Pattern Recognit. Lett., vol. 33, no. 12, pp. 1601-1613, 2012.