An enhanced background estimation algorithm for vehicle detection in urban traffic scenes

IEEE Transactions on Vehicular Technology

Volumn 59, Issue 8, 2010, Pages 3694-3709

  Vargas, Manuel ^a   Milla, Jose Manuel ^a   Toral, Sergio L ^a   Barrero, Federico ^a  

^a UNIVERSITY OF SEVILLE   (Spain)

Author keywords

Background estimation; sigma delta filter; urban environments; vehicle detection

Indexed keywords

BACKGROUND ESTIMATION; BACKGROUND MODEL; BACKGROUND SUBTRACTION ALGORITHMS; COMPARATIVE TESTS; COMPUTATIONAL COSTS; FOREGROUND DETECTION; MODEL UPDATING; PERFORMANCE EVALUATION; PIXEL LEVEL; PROCESSING STEPS; SIGMA-DELTA; SIGMA-DELTA FILTER; TYPICAL URBAN; URBAN ENVIRONMENTS; URBAN SCENES; URBAN TRAFFIC; VEHICLE DETECTION;

ALGORITHMS; ESTIMATION; MODELS; PIXELS; URBAN PLANNING; VEHICLES;

COMPUTATIONAL EFFICIENCY;

EID: 77958082524     PISSN: 00189545     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVT.2010.2058134     Document Type: Article

References (52)

1. P. G. Michalopoulos, "Vehicle detection video through image processing: The autoscope system," IEEE Trans. Veh. Technol., vol. 40, no. 1/2, pp. 21-29, Feb. 1991.
2. M. Fathy and M. Siyal, "A window-based image processing technique for quantitative and qualitative analysis of road traffic parameters," IEEE Trans. Veh. Technol., vol.47, no.4, pp. 1342-1349, Nov. 1998.
3. D. M. Ha, J.-M. Lee, and Y.-D. Kim, "Neural-edge-based vehicle detection and traffic parameter extraction," Image Vis. Comput., vol.22, no.11, pp. 899-907, Sep. 2004.
4. Z. Zhu and G. Xu, "VISATRAM: A real-time vision system for automatic traffic monitoring," Image Vis. Comput., vol.18, no.10, pp. 781-794, Jul. 2000.
5. J. Zhou, D. Gao, and D. Zhang, "Moving vehicle detection for automatic traffic monitoring," IEEE Trans. Veh. Technol., vol.56, no.1, pp. 51-59, Jan. 2007.
6. M. Piccardi, "Background subtraction techniques: A review," in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2004, vol.4, pp. 3099-3104.
7. D. Beymer, P. Mclauchlan, B. Coifman, and J. Malik, "A real-time computer vision system for measuring traffic parameters," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 1997, pp. 495-501.
8. N. K. Kanhere and S. T. Birchfield, "Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features," IEEE Trans. Intell. Transp. Syst., vol.9, no.1, pp. 148-160, Mar. 2008.
9. A. Prati, I. Mikic, M. M. Trivedi, and R. Cucchiara, "Detecting moving shadows: Algorithms and evaluation," IEEE Trans. Pattern Anal. Mach. Intell., vol.25, no.7, pp. 918-923, Jul. 2003.
10. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, "Detecting moving objects, ghosts, and shadows in video streams," IEEE Trans. Pattern Anal. Mach. Intell., vol.25, no.10, pp. 1337-1342, Oct. 2003.
11. P. Spagnolo, T. D. Orazio, M. Leo, and A. Distante, "Moving object segmentation by background subtraction and temporal analysis," Image Vis. Comput., vol.24, no.5, pp. 411-423, May 2006.
12. T. H. Chalidabhongse, K. Kim, D. Harwood, and L. Davis, "A perturbation method for evaluating background subtraction algorithms," in Proc. Joint IEEE Int. Workshop Vis. Surveillance Perform. Eval. Tracking Surveillance, Nice, France, 2003, pp. 1-7.
13. S.-C. Cheung and C. Kamath, "Robust techniques for background subtraction in urban traffic video," in Proc. SPIE Electron. Imaging Video Commun. Image Process., San Jose, CA, Jan. 2004, pp. 1-12.
14. C. R. Wren, A. Azarbayejani, T. J. Darrell, and A. P. Pentland, "Pfinder: Real-time tracking of the human body," IEEE Trans. Pattern Anal. Mach. Intell., vol.19, no.7, pp. 780-785, Jul. 1997.
15. D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, "Towards robust automatic traffic scene analysis in real-time," in Proc. ICPR, Nov. 1994, pp. 126-131.
16. B. P. L. Lo and S. A. Velastin, "Automatic congestion detection system for underground platforms," in Proc. ISIMP, May 2001, pp. 158-161.
17. Q. Zhou and J. Aggarwal, "Tracking and classifying moving objects from videos," in Proc. IEEE Workshop Perform. Eval. Tracking Surveillance, 2001, pp. 52-59.
18. C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 1999, vol.2, pp. 246-252.
19. C. Stauffer and W. Grimson, "Learning patterns of activity using realtime tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.8, pp. 747-757, Aug. 2000.
20. M. Harville, "A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models," in Proc. Eur. Conf. Comput. Vis., May 2002, vol.3, pp. 543-560.
21. W. Power and J. A. Schoonees, "Understanding background mixture models for foreground segmentation," in Proc. IVCNZ, Nov. 2002, pp. 267-271.
22. A. Elgammal, D. Harwood, and L. Davis, "Non-parametric model for background subtraction," in Proc. IEEE ICCV Frame-Rate Workshop, Sep. 1999, pp. 1-15.
23. A. Mittal and N. Paragios, "Motion-based background subtraction using adaptive kernel density estimation," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2004, pp. 302-309.
24. F. Porikli and O. Tuzel, "Bayesian background modeling for foreground detection," in Proc. ACM Vis. Surveillance Sens. Netw., 2005, pp. 55-58.
25. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-time foreground-background segmentation using codebook model," Real-Time Imag., vol.11, no.3, pp. 172-185, Jun. 2005.
26. A. Ilyas, M. Scuturici, and S. Miguet, "Real-time foreground-background segmentation using a modified codebook model," in Proc. Adv. Video Signal Based Surveillance, 2009, pp. 454-459.
27. I. Haritaoglu, D. Harwood, and L. Davis, "W4: Real-time surveillance of people and their activities," IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.8, pp. 809-830, Aug. 2000.
28. K.-P. Karmann and A. Brandt, "Moving object recognition using an adaptive background memory," in Time-Varying Image Processing and Moving Object Recognition, V. Cappellini, Ed. Amsterdam, The Netherlands: Elsevier, 1990, pp. 289-307.
29. D. Koller, J. Weber, and J. Malik, "Robust multiple car tracking with occlusion reasoning," in Proc. ECCV, Stockholm, Sweden, May 1994, pp. 189-196.
30. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and practice of background maintenance," in Proc. ICCV, Kerkyra, Greece, Sep. 1999, pp. 255-261.
31. A. Monnet, A. Mittal, A. Paragios, and V. Ramesh, "Background modelling and subtraction of dynamic scenes," in Proc. ICCV, Nice, France, Oct. 2003, pp. 1305-1312.
32. J. Zhong and S. Sclaroff, "Segmenting foreground objects from a dynamic, textured background via a robust Kalman filter," in Proc. ICCV, Nice, France, Oct. 2003, pp. 44-50.
33. N. M. Oliver, B. Rosario, and A. P. Pentland, "A Bayesian computer vision system for modeling human interactions," IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.8, pp. 831-843, Aug. 2000.
34. S. Toral, M. Vargas, and F. Barrero, "Embedded multimedia processors for road-traffic parameter estimation," Computer, vol.42, no.12, pp. 61-68, 2009.
35. N. McFarlane and C. Schofield, "Segmentation and tracking of pigglets in images," Mach. Vis. Appl., vol.8, no.3, pp. 187-193, May 1995.
36. A. Manzanera and J. C. Richefeu, "A robust and computationally efficient motion detection algorithm based on sigma-delta background estimation," in Proc. ICVGIP, Dec. 2004, pp. 46-51.
37. A. Manzanera and J. C. Richefeu, "A new motion detection algorithm based on sigma-delta background estimation," Pattern Recognit. Lett., vol.28, no.3, pp. 320-328, Feb. 2007.
38. A. Manzanera, Background Subtraction and the Zipf Law. Berlin, Germany: Springer-Verlag, 2008, pp. 42-51.
39. H. J. A. M. Heijmans, "Connected morphological operators for binary images," Comput. Vis. Image Understand., vol.73, no.1, pp. 99-120, Jan. 1999.
40. L. Vincent, "Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms," IEEE Trans. Image Process., vol.2, no.2, pp. 176-201, Apr. 1993.
41. P. Salembier and J. Ruiz, "Connected operators based on reconstruction process for size and motion simplification," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2002, vol.4, pp. 3289-3292.
42. S. Toral, M. Vargas, F. Barrero, and M. G. Ortega, "Improved sigma-delta background estimation for vehicle detection," Electron. Lett., vol.45, no.1, pp. 32-34, Jan. 2009.
43. i-LIDS Dataset for AVSS 2007. [Online]. Available: ftp://motinas.elec. qmul.ac.uk/pub/iLids
44. J. Davis and M. Goadrich, "The relationship between Precision-recall and ROC curves," in Proc. 23rd ICML, 2006, pp. 233-240.
45. P. L. Rosin and E. Ioannidis, "Evaluation of global image thresholding for change detection," Pattern Recognit. Lett., vol.24, no.14, pp. 2345-2356, Oct. 2003.
46. B. White and M. Shah, "Automatically tuning background subtraction parameters using particle swarm optimization," in Proc. IEEE ICME, Beijing, China, 2007, pp. 1826-1829.
47. A. Ilyas, M. Scuturici, and S. Miguet, "Real time foreground-background segmentation using a modified Codebook model," in Proc. 6th IEEE Int. Conf. AVSS, 2009, pp. 454-459.
48. P. Sneath and R. Sokal, Numerical Taxonomy. The Principle and Practice of Numerical Classification. San Francisco, CA: Freeman, 1973.
49. G. J. Brostow, J. Fauqueur, and R. Cipolla, "Semantic object classes in video: A high-definition ground truth database," Pattern Recognit. Lett., vol.30, no.2, pp. 88-97, Jan. 2009.
50. Open Computer Vision Library (OpenCV 2.0). [Online]. Available: http:// sourceforge.net/projects/opencvlibrary/
51. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision With the OpenCV Library. Wheeling, IL: O'Reilly, 2008.
52. F. Barrero, S. Toral, M. Vargas, F. Cortés, and J. M. Milla, "Internet in the development of future road-traffic control systems," Internet Res., vol.20, no.2, pp. 154-168, 2010.