Background Modeling - Package

Note: This page gives links to available implementation on background modeling. The number in parenthesis indicate the number of methods implemented.

Background Modeling - Package (6)

Code C++ (D. Parks - Department of Electrical and Computer Engineering - University of British Columbia - Canada - 2007)

S. Calderara, R. Melli, A. Prati, R. Cucchiara, “Reliable background suppression for complex scenes”, ACM international workshop on Video surveillance and sensor networks, VSSN 2006, pages 211-214, Santa Barbara, California, USA, 2006.

N. McFarlane, C. Schofield. "Segmentation and tracking of piglets in images", British Machine Vision and Applications, pages 187-193, 1995.

C. Wren, A. Azarbayejani, T. Darrell, A. Pentland, “Pfinder : Real-Time Tracking of the Human Body”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 19, No. 7, pages 780 –785 , July 1997.

C. Stauffer, E. Grimson, “Adaptive background mixture models for real-time tracking”, IEEE Conference on Computer Vision and Pattern Recognition ,CVPR 1999, pages 246-252, 1999.

Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction”, International Conference Pattern Recognition, Volume 2, pages 28-31, 2004.

N. Oliver, B. Rosario, A. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions” Proceedings of International Conference on Vision Systems, ICVS 1999, Gran Canaria, Spain, January 1999.

Background Modeling - Package (5)

Code C++ (L. Bender - Universidad Nacional de Tres de Febrero - Argentina - 2012)

C. Stauffer, E. Grimson, “Adaptive background mixture models for real-time tracking”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 1999, pages 246-252, 1999.

M. Sigari, N. Mozayani, H. Pourreza. Fuzzy Running Average and Fuzzy Background Subtraction: Concepts and Application, International Journal of Computer Science and Network Security, Vol. 8, No. 2, pp. 138-143, 2008.

L. Maddalena, A. Petrosino. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Transactions on Image Processing, Vol. 17, No. 7, pp. 1168-1177, 2008.

L. Maddalena, A. Petrosino. A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection. Neural Computing and Applications, Vol. 19, No. 2, pp. 179-186, 2010.

Background Modeling - Package

Motion Meerkat (B. Weinstein, Oregon State University, USA)

B. Weinstein, "Motionmeerkat: integrating motion video detection and ecological monitoring", Methods in Ecology and Evolution, 2014.