Regularised region-based Mixture of Gaussians for dynamic background modelling

11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014

Volumn , Issue , 2014, Pages 19-24

  Varadarajan, Sriram ^a   Wang, Hongbin ^a   Miller, Paul ^a   Zhou, Huiyu ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; GRADIENT METHODS; MIXTURES; MOMENTUM; PETROLEUM RESERVOIR EVALUATION; SECURITY SYSTEMS;

BACKGROUND MODELLING; DYNAMIC BACKGROUND MODELLING; EXPERIMENTAL EVALUATION; MIXTURE OF GAUSSIANS; ONLINE GRADIENT METHOD; REGULARISATION; SPEED OF CONVERGENCE; VIDEO SEQUENCES;

IMAGE SEGMENTATION;

EID: 84909944385     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AVSS.2014.6918638     Document Type: Conference Paper

References (17)

1. T. Andersson, G. Lathen, R. Lenz, and M. Borga. Modified gradient search for level set based image segmentation. IEEE Transactions on Image Processing, 22:621-630, 2013.
2. A. Beck and M. Teboulle. A fast iterative shrinkage thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2:183-202, 2009.
3. D. P. Bertsekas. Convex optimization theory by supplementary chapter 6 on convex optimization algorithms, 2013.
4. L. Bottou. Online algorithms and stochastic approximations. In Online Learning and Neural Networks. Cambridge University Press, 1998.
5. J. Domke. Generic methods for optimization-based modeling. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), pages 318-326, 2012.
6. H. Le, L. Hu, and Y. Feng. Momentum based level set method for accurate object tracking. International Journal of Intelligent Systems and Applications, 2:10-16, 2010.
7. L. Li, W. Huang, I. Y. H. Gu, and Q. Tian. Foreground object detection from videos containing complex background. In Proceedings of the eleventh ACM international conference on Multimedia, pages 2-10, 2003.
8. Y. Nesterov. Introductory lectures on convex optimization. a basic course, 2004.
9. B. T. Polyak. Introduction to Optimization. Optimization Software, Inc., Publications Divisions, New York, 1987.
10. H. Robbins and S. Monro. A stochastic approximation method. The Annals of Mathematical Statistics, 22:400-407, 1951.
11. C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for real-time tracking. In IEEE Computer Soci-ety Conference on Computer Vision and Pattern Recognition, pages 2246-2252, 1999.
12. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In Seventh International Conference on Computer Vision, pages 255-261, 1999.
13. P. Tseng. On accelerated proximal gradient methods for convex-concave optimization. SIAM Journal on Optimization, 2008.
14. S. Varadarajan, P. Miller, and H. Zhou. Spatial mixture of gaussians for dynamic background modelling. In Ad-vanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference on, pages 63-68, Aug 2013.
15. H. Wang and P. Miller. Regularized online mixture of gaussians for background subtraction. In 8th IEEE International Conference on Advanced Video and Signal Based Surveil-lance (AVSS), pages 249-254, 2011.
16. H. Wang and P. Miller. Scaled heavy-ball acceleration of the richardson-lucy algorithm for 3d microscopy image restoration. IEEE Transactions on Image Processing, 23:848-854, 2014.
17. J. Zhong and S. Sclaroff. Segmenting foreground objects from a dynamic textured background via a robust kalman filter. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 44-50, October 2003.