A non-parametric pixel-based background modeling for dynamic scenes

2009 International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2009

Volumn , Issue , 2009, Pages 369-373

  Armanfard, N ^a   Komeili, M ^a   Valizade, M ^a   Kabir, E ^a   Jalili, S ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Background modeling; Background subtraction; Non parametric; Non stationary scenes; Surveillance systems

Indexed keywords

BACKGROUND MODELING; BACKGROUND SUBTRACTION; NON-PARAMETRIC; NON-STATIONARY SCENES; SURVEILLANCE SYSTEMS;

COMPUTATIONAL EFFICIENCY; MODELS; MONITORING; SECURITY SYSTEMS; VIDEO RECORDING;

PIXELS;

EID: 70350510933     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACTEA.2009.5227894     Document Type: Conference Paper

References (23)

1. S. Fejes, L. S. Davis, "What can projections of flow fields tell us about the visual motion." Bombay, India, ICCV'98, 4-7 January.
2. N. Paragios, R. Deriche, Geodesic active contours and level sets for the detection and tracking of moving objects, IEEE Transactions on Pattern Analysis and Machine Interface, 22(3) (2000) 266-280,
3. M. Komeili, N. Armanfard , E. Kabir, Adaptive visual tracking by decision level fusion of features in a particle filtering frame work, in: The Fifth Iranian Conference on Machine Vision and Image Processing, 2008.
4. M. Komeili, N. Armanfard , E. Kabir, A fuzzy approach for multi-feature pedestrian tracking with particle filter, in: EEE International Symposium on Telecommunications, 2008.
5. N. Martel-Brisson, A. Zaccarim, Moving cast shadow detection from a gaussian mixture shadow model, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2005.
6. M.Komeili, E. Kabir, robust color-based pedestrian tracking in varying illumination invironment, in: iranian conferance on electrical engineering, tehran, 2008.
7. L. Li, W. Huang, I. Y.-H. Gu , Q. Tian, Statistical modeling of complex backgrounds for foreground object detection, IEEE Transactions on Image Processing 13 (2004) 1459-1472,
8. C. Stauffer, W. E. L. Grimson, Adaptive background mixture models for real-time tracking, in: Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 1999.
9. D. S. Lee, Effective gaussian mixture learning for video background subtraction, IEEE Trans. Pattern Anal. Mach. Intell., 27(5) (2005) 827-835,
10. A. Shimada, D. Arita, Dynamic Control of Adaptive Mixture-of-Gaussians Background Model, in: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (AVSS'06), 2006.
11. J. Wang, G. Bebis , R. Miller, Robust Video-Based Surveillance by Integrating Target Detection with Tracking, in: Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop., 2006.
12. C. Ridder, O. Munkelt , H. Kirchner, Adaptive background estimation and foreground detection using kalman-filtering, in: Proceedings of International Conference on Recent Advances in Mechatronics, 1995.
13. B. Stenger, V. Ramesh, N. Paragios, F. Coetzee , J. M. Buhmann, Topology free hidden Markov models: application to background modeling, in: Proceedings of IEEE International Conference on Computer Vision, 2001.
14. S. Jabri, Z. Duric, H. Wechsler , A. Rosenfeld, Detection and location of people in video images using adaptive fusion of color and edge information, in: 15th International Conference on Pattern Recognition, 2000.
15. W. Zhang, X. Zhong , F. Y. Xu;, Detection of moving cast shadows using image orthogonal transform, in: Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), 2006.
16. M. Heikkilä, M. Pietäikinen, A texture-based method for modeling the background and detecting moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006) 657-662,
17. M. Heikkilä, M. Pietäikinen , J. Heikkilä, A texture-based method for detecting moving objects, in: Proceedings of British Machine Vision Conference, 2004.
18. Y.-T. Chen, C.-S. Chen, C.-R. Huang , Y.-P. Hung, Efficient hierarchical method for background subtraction, Pttern Recognition, 40 (2007) 2706-2715,
19. M. Mason, Z. Duric, Using histograms to detect and track objects in color video, in: Proceedings of 30th Applied Imagery Pattern Recognition Workshop, 2001.
20. N. Armanfard, M. Komeili , E. Kabir, TED: A Texture-Edge Descriptor based on LBP for pedestrian detection, in: IEEE International Symposium on Telecommunications, 2008.
21. T. Ojala, M. Pietikainen , T. Mäenpää, Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002) 971-987,
22. F. Tajeripour, E. Kabir, Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns, in: IEEE International Conference on Computational Intelligence and Multimedia Applications, 2007.
23. Y.-T. Chen, C.-S. Chen, C.-R. Huang , Y.-P. Hung, Efficient hierarchical method for background subtraction, Pattern Recognition, 40 (2007) 2706-2715,