Robust foreground segmentation using improved Gaussian mixture model and optical flow

2012 International Conference on Informatics, Electronics and Vision, ICIEV 2012

Volumn , Issue , 2012, Pages 248-253

  Fradi, Hajer ^a   Dugelay, Jean Luc ^a  

^a EURECOM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND MODEL ESTIMATION; BACKGROUND SUBTRACTION; COMPLEX BACKGROUND; FOREGROUND SEGMENTATION; GAUSSIAN MIXTURE MODEL; MOTION MODELS; MOVING OBJECTS; PROBABILISTIC APPROACHES; SEGMENTATION PERFORMANCE; VIDEO-SURVEILLANCE APPLICATIONS;

INFORMATION SCIENCE;

SECURITY SYSTEMS;

EID: 84869379919     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIEV.2012.6317376     Document Type: Conference Paper

References (20)

1. R. Jain and H. H. Nagel. On the analysis of accumulative difference pictures from image sequences of real world scenes. IEEE Trans. Pattern Analysis and Machine Intelligence, 1(2):206-214, 1979.
2. M. Cristani, M. Farenzena, D. Bloisi, and V. Murino. Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP Journal on Advances in Signal Processing, 2010, 2010.
3. T. J. Ellis, P. L. Rosin, and P. Golton. Model-based vision for automatic alarm interpretation. IEEE Aerospace and Electronic Systems Magazine, 6(3):14-20, 1991.
4. S. B. Gray. Local properties of binary images in two dimensions. IEEE Trans. Computers, 20(5):551-561, 1971.
5. C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland. Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):780-785, 1997.
6. C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for real-time tracking. Proc. of CVPR, 246-252, 1999.
7. Y. T. Chen, C. S. Chen, C. R. Huang, and Y. P. Hung. Efficient hierarchical method for background subtraction. Pattern Recognition, 40(10):2706-2715, 2007.
8. E. Hayman and J. O. Eklundh. Statistical background subtraction for a mobile observer. International Conference on Computer Vision, 2003.
9. Z. Zivkovic and F. van der Heijden. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, 27(7):773-780, 2006.
10. D. Zhou and H. Zhang. Modified GMM background modeling and optical flow for detection of moving objects. International Conf. on Systems, Man and Cybernetics, 3(5):2224-2229, 2005.
11. X. Cai, F. H. Ali, and E. Stipidis. Robust online video background reconstruction using optical flow and pixel intensity distribution. IEEE International Conference on Communications-Signal Processing for Communications Symposium, 1-5, 2008.
12. M. S. Kemouche and N. Aouf. A gaussian mixture based optical flow modeling for object detection. ICDP, 1-6, 2009.
13. C. Wolf and J. M. Jolion. Integrating a discrete motion model into gmm based background subtraction. ICPR, 9-12, 2010.
14. P. Kaewtrakulpong and R. Bowden. An improved adaptive background mixture model for realtime tracking with shadow detection. 2nd European Workshop on Advanced Video Based Surveillance Systems, 2001.
15. Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. ICPR, 2:28-31, 2004.
16. A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara. Detecting moving shadows: Algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, (25):918-923, 2003.
17. B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. Proc. of the 7th International Joint Conference on Artificial Intelligence, 674-679, 1981.
18. B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981.
19. G. Farneback. Two-frame motion estimation based on polynomial expansion. Proc. of 13th Scandinavian Conference on Image Analysis, 363-370, 2003.
20. W. Huang, I. Y. H. Gu, and Q. Tian. Foreground object detection from videos containing complex background. ACM MM, 2003.