Moving/motionless foreground object detection using fast statistical background updating

REFERENCES

• Hu W, Tan T, Wang L, Maybank S. A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev., 2004, 34, 334–352.
   PubMed Web of Science ®Google Scholar
• Horn BKP, Schunck BG. Determining optical flow. Artif. Intell., 1981, 17, 185–203.
   Web of Science ®Google Scholar
• Lucas BD, Kanade T. An iterative image registration technique with an application to stereo vision, Proc. PADRA Imaging Understanding Workshop, Washington, DC, USA, April 1981, IEEE, pp. 121–130.
   Google Scholar
• Gibson D, Spann M. Robust optical flow estimation based on a sparse motion trajectory set. Image Process., 2003, 12, 431–445.
   PubMed Web of Science ®Google Scholar
• Liu PR, Meng MQ.-H, Liu PX.: Moving object segmentation and detection for monocular robot based on active contour model. Electron. Lett., 2005, 41, 1320–1322
   Web of Science ®Google Scholar
• Kim YH, Kak AC. Error analysis of robust optical flow estimation by least median of squares methods for the varying illumination model. Patt. Anal. Mach. Intell., 2006, 28, 1418–1435.
   PubMed Web of Science ®Google Scholar
• Chamorro-Martinez J, Fernandez-Valdivia J. A new approach to motion pattern recognition and its application to optical flow estimation. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev., 2007, 37, 39–51.
   Web of Science ®Google Scholar
• Lipton AJ, Fujiyoshi H, Patil RS. Moving target classification and tracking from real-time video, Proc. 4th IEEE Workshop on Applications of computer vision: WACV ’98, Princeton, NJ, USA, October 1998, IEEE, pp. 8–14.
   Google Scholar
• Wang CM, Brandstein MS. A hybrid real-time face tracking system, Proc. IEEE Int. Conf. on Acoustics, speech, signal processing: ICASSP ’98, Seattle, WA, USA, May 1998, IEEE, pp. 3737–3740.
   Google Scholar
• Piccardi M. Background subtraction techniques: a review, Proc. IEEE Int. Conf. on Systems, man, cybernetics, The Hague, The Netherlands, October 2004, IEEE, pp. 3099–3104.
   Google Scholar
• Wren CR, Azarbayejani A, Darrell T, Pentland AP. Pfinder: real-time tracking of the human body. IEEE Trans. Patt. Anal. Mach. Intell., 1997, 19, 780–785.
   Web of Science ®Google Scholar
• Olson T, Brill F. Moving object detection and event recognition algorithm for smart camera, Proc. DARPA Image Understanding Workshop, New Orleans, LA, USA, May 1997, Morgan Kaufmann, pp. 159–175.
   Google Scholar
• Eveland C, Konolige K, Bolles RC. Background modeling for segmentation of video-rate stereo sequences, Proc. IEEE Conf. on Computer vision and pattern recognition: CVPR ’98, Santa Barbara, CA, USA, June 1998, IEEE Computer Society, pp. 266–271.
   Google Scholar
• Kanade T, Collins R, Lipton A, Burt P, Wixson L. Advances in cooperative multi-sensor video surveillance, Proc. DARPA Image Understanding Workshop, CA, MontereyUSA, November1998, IEEE, Vol. 1, pp. 3–24.
   Google Scholar
• Cavallaro A, Ebrahimi T. Video object extraction based on adaptive background and statistical change detection. Proc. SPIE, 2001, 4310, 465–475.
   Google Scholar
• Stauffer C, Grimson WEL. Adaptive background mixture models for real-time tracking, Proc. IEEE Conf. on Computer vision and pattern recognition: CVPR ’99, IEEE Computer Society, Vol. 2, pp. 246–252.
   Google Scholar
• Stauffer C, Grimson WEL. Learning patterns of activity using real-time tracking. IEEE Trans. Patt. Anal. Mach. Intell., 2000, 22, 747–757.
   Web of Science ®Google Scholar
• KaewTrakulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection, Proc. 2nd European Workshop on Advanced video based surveillan: AVBS 2001, Kingston, UK, September 2001, Kingston University, pp. 149–158.
   Google Scholar
• Lee D.-S. Effective Gaussian mixture learning for video background subtraction. IEEE Trans. Patt. Anal. Mach. Intell., 2005, 27, 827–832.
   PubMed Web of Science ®Google Scholar
• McFarlane N, Schofield C. Segmentation and tracking of piglets in images. Mach. Vision Appl., 1995, 8, 187–193.
   Web of Science ®Google Scholar
• Manzanera A, Richefeu JC. A new motion detection algorithm based on Σ–Δ background estimation. Patt. Recogn. Lett., 2007, 28, 320–328.
   Web of Science ®Google Scholar
• Elgammal A, Duraiswami R, Harwood D, Davis LS. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE, 2002, 90, 1151–1163.
   Web of Science ®Google Scholar
• Ianasi C, Gui V, Toma CI, Pescaru D. A fast algorithm for background tracking in video surveillance using nonparametric kernel density estimation. Facta Univ. Ser.: Electron. Energ., 2005, 18, 127–144.
   Google Scholar
• Elgammal A, Duraiswami R, Davis L. Efficient kernel density estimation using the Fast Gauss Transform with applications to color modeling and tracking. IEEE Trans. Patt. Anal. Mach. Intell., 25, 1499–1504.
   Web of Science ®Google Scholar
• Greengard L, Strain J. The fast Gauss transform. SIAM J. Sci. Stat. Comput., 1991, 12, 79–94.
   Web of Science ®Google Scholar
• Huerta I, Rowe D, Gonzalez J, Villanueva JJ. Improving foreground detection for adaptive background segmentation, Proc. 1st CVC Workshop on computer vision: progress of research and development: CVCRD 2006, Bellaterra, Spain, Universitat Autònoma de Barcelona.October 2006, pp. 1–6.
   Google Scholar
• Bayona A, SanMiguel JC, Martiínez JM. Stationary foreground detection using background subtraction and temporal difference in video surveillance, Proc. 17th IEEE Int. Conf. on Image processing: ICIP 2010, Hong Kong, China, September 2010, IEEE Signal Processing Society pp. 4657–4660.
   Google Scholar
• Porikli F, Yin Z. Temporally static region detection in multi-camera systems, Proc. 10th IEEE Int. Workshop on Performance evaluation of tracking and surveillance: PETS 2007, Rio de Janeiro, Brazil, October 2007, IEEE, pp. 79–86.
   Google Scholar
• Montgomery DC. Introduction to Statistical Quality Control, 2004 (John Wiley & Sons, New York).
   Google Scholar
• Hsieh KL, Tong LI, Wang MC. The application of control chart for defects and defect clustering in IC manufacturing based on fuzzy theory. Expert Syst. Appl., 2007, 32, 765–776.
   Web of Science ®Google Scholar
• OpenCV, Intel Open Source Computer Vision Library. <http://www.intel.com/technology/computing/opencv/>
   Google Scholar
• Toyama K, Krumm J, Brumitt B, Meyers B. Wallflower: principles and practice of background maintenance, Proc. 7th IEEE Int. Conf. on Computer vision: ICCV ’99: Corfu, Greece, September1999, IEEE Computer Society, pp. 255–261.
   Google Scholar