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Computación y Sistemas

versión On-line ISSN 2007-9737versión impresa ISSN 1405-5546

Comp. y Sist. vol.19 no.2 Ciudad de México abr./jun. 2015

https://doi.org/10.13053/CyS-19-2-2006 

Artículos

 

Filtro mediana recursivo para la estimación de fondo y segmentación de primer plano en videos de vigilancia

 

Recursive Median Filter for Background Estimation and Foreground Segmentation in Surveillance Videos

 

Freddy Alexander Díaz González y David Alejandro Arévalo Suárez

 

Universidad Sergio Arboleda, Colombia. freddy.diaz@correo.usa.edu.co, david.arevalo@correo.usa.edu.co

Autor de correspondencia es Freddy A. Díaz González.

 

Artículo recibido el 13/08/2014.
Aceptado el 17/04/2015.

 

Resumen

El uso de cámaras de video es ampliamente usado en los sistemas de vigilancia, y ofrece la posibilidad de realizar el procesamiento de las imágenes capturadas para la detección automática de eventos de interés que se puedan presentar en la escena. El siguiente trabajo propone un método de estimación del fondo y segmentación del primer plano en videos de vigilancia, mediante el uso de un filtro mediana recursivo, con la aplicación de una ventana móvil temporal en la cantidad de fotogramas a analizar, que ofrezcan una mayor robustez frente al ruido causado por los cambios de iluminación y vibraciones de la cámara, limitando el incremento del costo computacional durante el procesamiento.

Palabras clave: Mediana temporal, estimación de fondo, primer plano, recurrencia.

 

Abstract

Video cameras are widely used in surveillance systems; this offers the possibility of processing the captured images for automatic detection of events of interest that may arise in the scene. The present paper proposes a method for estimating the background and foreground segmentation in video surveillance using a recursive median filter and applying a temporal moving window in the number of frames to be analyzed, which provide more robustness against noise caused by changes in illumination and camera shake, limiting the increase in the computational cost of processing.

Keywords: Temporal median, background subtraction, foreground, recurrence.

 

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Referencias

1. Guan, Y.P, Du, J.H., & Zhang, C.Q. (2012). Improved HSV-Based Gaussian Mixture Modeling for Moving Foreground Segmentation. pp. 52-58.         [ Links ]

2. Pava, D.F. (2011). Object Detection and Motion Analysis in a Low Resolution 3-D Model. Ninth LACCEI Latin American and Caribbean Conference (LACCEI'2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, Medellín, Colombia.         [ Links ]

3. Kim, Z. (2008). Real Time Object Tracking based on Dynamic Feature Grouping. Computer Vision and Pattern Recognition, IEEE Conference on, Anchorage, AK, 2008, pp. 1-8.         [ Links ]

4. CCTV User Group (2011). Two million cameras in the UK. CCTV Image, pp. 10-12.         [ Links ]

5. Ferrando, S. (2006). Classification of Unattended and Stolen Objects in Video-Surveillance System. Video and Signal Based Surveillance, Sydney, Australia.         [ Links ]

6. Plataniotis, K.N. (2005). Visual-centric Surveillance Networks and Services. IEEE Signal Processing Magazine, Vol. 22, No. 2, pp. 12-15.         [ Links ]

7. Mahadevan, V. (2008). Background Subtraction in Highly Dynamic Scenes. Conf. on Computer Vision and Pattern Recognition (p. 1). Anchorage, AK: IEEE.         [ Links ]

8. Parks, D.H. (2008). Evaluation of Background Subtraction Algorithms with Post-processing. Advanced Video and Signal Based Surveillance, (AVSS '08), IEEE Fifth International Conference on, pp. 192-199, Santa Fe, NM.         [ Links ]

9. Zhang, Y.J. (2006). Advances in Image and Video Segmentation. IRM Press.         [ Links ]

10. González, R.C., Woods, R.E., & Eddins, S.L. (2002). Digital image processing (2nd Edition). Englewood Cliffs: Prentice Hall.         [ Links ]

11. Kamijo, S. (2000). Occlusion robust tracking utilizing spatio-temporal Markov random field model. ICPR, Vol.1, pp. 11-40.         [ Links ]

12. Zhang, Y.J., & Lu, H.B. (2002). A hierarchical organization scheme for video data. Pattern Recognition, Vol. 35, No.11, pp. 2381-2387.         [ Links ]

13. McHugh, J.M. (2009). Foreground-Adaptive Background Subtraction. Signal Processing Letters, IEEE, pp. 390-393. DOI: 10.1109/ LSP.2009.2016447        [ Links ]

14. Toyama, K. (1999). Wallflower: principles and practice of background maintenance. Computer Vision, Proceedings of the Seventh IEEE International Conference on, Vol. 1, pp. 255-261.         [ Links ]

15. Lo, B. (2001). Automatic congestion detection system for underground platforms. Intelligent Multimedia, Video and Speech Processing, Proceedings of 2001 International Symposium on, pp. 158-161. DOI: 10.1109/ISIMP.2001.925356        [ Links ]

16. Cucchiara, R. (2003). Detecting moving objects, ghosts, and shadows in video streams. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 25, No. 10, pp. 1337-1342. DOI: 10.1109/TPAMI.2003.1233909        [ Links ]

17. Caldera, S. (2006). Reliable background suppression for complex scenes. Video surveillance and sensor networks, pp. 211-214. DOI: 10.1109/TPAMI.2003.1233909        [ Links ]

18. Maddalena, L. (2008). Background Subtraction in Highly Dynamic Scenes. Conf. on Computer Vision and Pattern Recognition (p. 1), Anchorage, AK: IEEE.         [ Links ]

19. Cheung, S.C.S. (2004). Robust techniques for background subtraction in urban traffic video. Visual Communications and Image Processing, 5308, pp. 881-892. DOI: 10.1117/12.526886        [ Links ]

20. Stauffer, C. (2000). Learning patterns of activity using real-time tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 22, No. 8, pp. 747-757. DOI: 10.1109/34.868677        [ Links ]

21. Tsai, D.M. (2009). Independent Component Analysis-Based Background. IEEE Transactions on Image Processing, Vol. 18, No. 1, pp. 158-167. DOI: 10.1109/TIP.2008.2007558        [ Links ]

22. Power, P. (2002). Understanding background mixture models for foreground segmentation. Proc. of the Image and Vision Computing, pp. 266-271.         [ Links ]

23. Zivkovic, Z. (2006). Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, Vol. 27, No. 7, pp. 773-780. DOI: 10.1016/j.patrec.2005.11.005        [ Links ]

24. Heikkila, J. (1999). A real-time system for monitoring of cyclists and pedestrians. Visual Surveillance, Second IEEE Workshop on, pp. 74-81. DOI: 10.1016/j.imavis.2003.09.010        [ Links ]

25. Aach, T. (1995). Bayesian algorithms for adaptive change detection in image sequences using Markov random fields. Signal Process. Vol. 7, pp. 147-160. DOI: 10.1016/0923-5965(95)00003-F        [ Links ]

26. Paragios, N. (2001). A MRF-based approach for real-time subway monitoring. Computer Vision and Pattern Recognition, Proceedings of the 2001 IEEE Computer Society Conference on, Volume 1, pp. 1034-1040. DOI: 10.1109/CVPR.2001.990644        [ Links ]

27. Migdal, J. (2005). Background Subtraction Using Markov Thresholds. Motion and Video Computing, (WACV/MOTIONS05), Volume 2, IEEE Workshop on, pp. 58-65. DOI: 10.1109/ACVMOT.2005.33        [ Links ]

28. Marco Cristani. (2010). Background Subtraction for Automated Multisensor Surveillance. EURASIP Journal on Advances in Signal Processing. DOI: 10.1155/2010/343057

29. Ahmed, E. (2002). Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance. Proc. IEEE, Vol. 90, pp. 1151-1163. DOI: 10.1109/JPROC. 2002.801448        [ Links ]

30. Ahmed, E. (2000). Non-parametric Model for Background Subtraction. ECCV '00 Proceedings of the 6th European Conference on Computer Vision, pp. 751-767. DOI: 10.1007/3-540-45053-X_48        [ Links ]

31. Friedman, N. (1997). Image segmentation in video sequences: a probabilistic approach. 13th Conference on Uncertainty in Artificial Intelligence, pp. 175-181.         [ Links ]

32. Stauffer, C. (2000). Learning patterns of activity using real-time tracking. Patterns Analysis and Machine Intelligence, IEEE Transactions on, Vol. 22, No. 8, pp. 747-757. DOI: 10.1109/34.868677        [ Links ]

33. Ahmed, E. (2001). Efficient non-parametric adaptive color modeling using fast Gauss transform. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, pp. 563-570. DOI: 10.1109/CVPR.2001.991012        [ Links ]

34. McFarlane, N.J. (1995). Segmentation and tracking of piglets in images. Machine Vision and Applications, Vol. 8, No. 3, pp. 187-193.         [ Links ]

35. Jung, C.R. (2009). Efficient Background Subtraction and Shadow Removal. Multimedia, IEEE Transactions on, pp. 571-577. DOI: 10.1109/TMM.2009.2012924        [ Links ]

36. Zhu, F. (2009). A Video-based Traffic Congestion Monitoring System Using Adaptive Background Subtraction. Electronic Commerce and Security, (ISECS '09), Second International Symposium on, pp. 73-77. DOI: 10.1109/ISECS.2009.64        [ Links ]

37. Knuth, Donald E. (2005). Art of Computer Programming. Addison-Wesley, United States.         [ Links ]

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