Moving object detection via robust low rank matrix decomposition with IRLS scheme

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7431 LNCS, Issue PART 1, 2012, Pages 665-674

  Guyon, Charles ^a   Bouwmans, Thierry ^a   Zahzah, El Hadi ^a  

^a UNIVERSITÉ DE LA ROCHELLE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONNEXITY; DATA SETS; ITERATIVELY REWEIGHTED LEAST SQUARES; LOW RANK; LOW-RANK MATRICES; MOVING OBJECTS; MOVING-OBJECT DETECTION; PRINCIPAL COMPONENTS ANALYSIS; VIDEO SURVEILLANCE SYSTEMS;

PRINCIPAL COMPONENT ANALYSIS; SECURITY SYSTEMS;

OBJECT RECOGNITION;

EID: 84866686830     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33179-4_63     Document Type: Conference Paper

References (15)

1. Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics
2. Bouwmans, T., Baf, F.E., Vachon, B.: Background modeling using mixture of gaussians for foreground detection - a survey. RPCS 1, 219-237 (2008)
3. Bouwmans, T.: Recent advanced statistical background modeling for foreground detection: A systematic survey. RPCS 4, 147-176 (2011)
4. Candes, E., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? International Journal of ACM 58 (2011)
5. Wright, J., Peng, Y., Ma, Y., Ganesh, A., Rao, S.: Robust principal component analysis: Exact recovery of corrupted low-rank matrices by convex optimization. In: Neural Information Processing Systems, NIPS 2009 (2009)
6. Daubechies, I., Devore, R., Fornasier, M., Gntrk, C.S.: Iteratively reweighted least squares minimization for sparse recovery. Comm. Pure Appl. Math. (2008)
7. Osborne, M.R.: Finite algorithms in optimization and data analysis. John Wiley & Sons (1985)
8. Tang, G., Nehorai, A.: Robust principal component analysis based on low-rank and block-sparse matrix decomposition. In: Annual Conference on Information Sciences and Systems, CISS 2011 (2011)
9. Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: NIPS 2011 (2011)
10. Ma, S.: Algorithms for sparse and low-rank optimization: Convergence, complexity and applications. Thesis (2011)
11. He, J., Balzano, L., Szlam, A.: Incremental gradient on the grassmannian for online foreground and background separation in subsampled video. In: International Conference on Computer Vision and Pattern Recognition (CVPR) (June 2012)
12. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: ICCV 1999, pp. 255-261 (1999)
13. Li, L., Huang, W., Gu, I., Tian, Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE Transaction on Image Processing, 1459-1472 (2004)
14. Jodoin, P., Porikli, F., Konrad, J., Ishwar, P.: Change detection benchmark web site. In: IEEE Workshop on Change Detection (June 2012)
15. Maddalena, L., Petrosino, A.: A fuzzy spatial coherence-based approach to background foreground separation for moving object detection. Neural Computing and Applications, 1-8 (2010)