The 3dSOBS+ algorithm for moving object detection

Computer Vision and Image Understanding

Volumn 122, Issue , 2014, Pages 65-73

  Maddalena, Lucia ^a   Petrosino, Alfredo ^b  

^a CNR   (Italy)

^b UNIVERSITY OF NAPLES PARTHENOPE   (Italy)

Author keywords

Background subtraction; Motion detection; Neural network; Self organization

Indexed keywords

AUTOMATICALLY GENERATED; BACKGROUND SUBTRACTION; ILLUMINATION VARIATION; MOTION DETECTION; MOVING-OBJECT DETECTION; SELF ORGANIZATIONS; SELF-ORGANIZING METHOD; STATE-OF-THE-ART METHODS;

LEARNING ALGORITHMS; NEURAL NETWORKS;

OBJECT RECOGNITION;

EID: 84898079786     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2013.11.006     Document Type: Article

References (29)

1. S.-c.S. Cheung, and C. Kamath Robust techniques for background subtraction in urban traffic video S. Panchanathan, B. Vasudev, Proc. VCIP vol. 5308 2004 SPIE 881 892
2. M. Piccardi, Background subtraction techniques: a review, in: Proc. IEEE SMC 2004, vol. 4, pp. 3099-3104.
3. R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam Image change detection algorithms: a systematic survey IEEE Trans. Image Process. 14 2005 294 307
4. S. Elhabian, K. El Sayed, and S. Ahmed Moving object detection in spatial domain using background removal techniques: State-of-art Recent Pat. Comput. Sci. 1 2008 32 54
5. N. Goyette, P. Jodoin, F. Porikli, J. Konrad, P. Ishwar, Changedetection.net: a new change detection benchmark dataset, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2012, pp. 1-8.
6. A. Vacavant, T. Chateau, A. Wilhelm, and L. Lequivre A benchmark dataset for outdoor foreground/background extraction J.-I. Park, J. Kim, Computer Vision - ACCV 2012 Workshops Lecture Notes in Computer Science vol. 7728 2013 Springer Berlin Heidelberg 291 300
7. A. Cretu, E. Petriu, P. Payeur, F. Khalil, Deformable object segmentation and contour tracking in image sequences using unsupervised networks, in: Proc. of Canadian Conference Computer and Robot Vision, IEEE, 2010, pp. 277-284.
8. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland Pfinder: real-time tracking of the human body Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition vol. 0 1996 IEEE Computer Society Los Alamitos, CA, USA 51
9. C. Stauffer, W. Grimson, Adaptive background mixture models for real-time tracking, in: Proc. CVPR 1999, vol. 2, p. 252.
10. K. Kim, T.H. Chalidabhongse, D. Harwood, and L. Davis Real-time foreground-background segmentation using codebook model Real-Time Imaging 11 2005 172 185
11. L. Maddalena, and A. Petrosino A self-organizing approach to background subtraction for visual surveillance applications IEEE Trans. Image Process. 17 2008 1168 1177
12. T. Bouwmans Background subtraction for visual surveillance: a fuzzy approach S. Pal, A. Petrosino, L. Maddalena, Handbook on Soft Computing for Video Surveillance 2012 Chapman & Hall/CRC 103 138
13. L. Maddalena, and A. Petrosino A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection Neural Comput. Appl. 19 2010 179 186
14. L. Maddalena, and A. Petrosino Neural networks in video surveillance: a perspective view S. Pal, A. Petrosino, L. Maddalena, Handbook on Soft Computing for Video Surveillance 2012 Chapman & Hall/CRC 59 78
15. L. Maddalena, and A. Petrosino Stopped object detection by learning foreground model in videos IEEE Trans. Neural Net. Learn. Sys. 24 2013 723 735
16. K. Toyama, J. Krumm, B. Brumitt, B. Meyers, Wallflower: principles and practice of background maintenance, in: Proc. ICCV 1999, vol. 1, pp. 255-261.
17. V. Reddy, C. Sanderson, and B.C. Lovell A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts J. Image Video Process. 2011 2011 1:1 1:14
18. B. Gloyer, H.K. Aghajan, K.-Y. Siu, T. Kailath, Video-based freeway-monitoring system using recursive vehicle tracking, in: Proc. SPIE 1995, vol. 2421, pp. 173-180.
19. R. Fisher, Change Detection in Color Images, Unpublished, 1999. < http://homepages.inf.ed.ac.uk/rbf/PAPERS/iccv99.pdf >.
20. P. Burt Fast filter transform for image processing Comput. Graphics Image Process. 16 1981 20 51
21. J. Ding, R. Ma, and S. Chen A scale-based connected coherence tree algorithm for image segmentation IEEE Trans. Image Process. 17 2008 204 216
22. J. Choi, Y.J. Yoo, and J.Y. Choi Adaptive shadow estimator for removing shadow of moving object Comput. Vis. Image Understan. 114 2010 1017 1029
23. P. KaewTraKulPong, and R. Bowden An improved adaptive background mixture model for real-time tracking with shadow detection P. Remagnino, G.A. Jones, N. Paragios, C.S. Regazzoni, Video-Based Surveillance Systems 2002 Springer US 135 144
24. Z. Zivkovic, Improved adaptive Gaussian mixture model for background subtraction, in: Proc. ICPR 2004, vol. 2, pp. 28-31.
25. M. Shah, J. Deng, and B. Woodford Illumination invariant background model using mixture of Gaussians and SURF features J.-I. Park, J. Kim, Computer Vision - ACCV 2012 Workshops Lecture Notes in Computer Science vol. 7728 2013 Springer Berlin Heidelberg 308 314
26. A. Glazer, M. Lindenbaum, and S. Markovitch One-class background model J.-I. Park, J. Kim, Computer Vision - ACCV 2012 Workshops Lecture Notes in Computer Science vol. 7728 2013 Springer Berlin Heidelberg 301 307
27. H. Rezazadegan Tavakoli, E. Rahtu, and J. Heikkila Temporal saliency for fast motion detection J.-I. Park, J. Kim, Computer Vision - ACCV 2012 Workshops Lecture Notes in Computer Science vol. 7728 2013 Springer Berlin Heidelberg 321 326
28. Y. Dong, and G. DeSouza Adaptive learning of multi-subspace for foreground detection under illumination changes Computer Vision and Image Understanding 115 2011 31 49
29. J. Choi, H.J. Chang, Y.J. Yoo, and J.Y. Choi Robust moving object detection against fast illumination change Comput. Vis. Image Und. 116 2012 179 193