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In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation.
Maximum likelihood from incomplete data via the em algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
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Adaptive background mixture models for real-time tracking
C. Stauffer and W. E. L. Grimson · 1999
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Wallflower: Principles and practice of background maintenance
K. Toyama, J. Krumm, B. Brumitt, and B. Meyers · 1999
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Non-parametric model for background subtraction
A. Elgammal, D. Harwood, and L. Davis · 2000
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A bayesian computer vision system for modeling human interactions
N. M. Oliver, B. Rosario, and A. P. Pentland · 2000
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Real-time foreground–background segmentation using codebook model
K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis · 2005
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A texture-based method for modeling the background and detecting moving objects
M. Heikkila and M. Pietikainen · 2006
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Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking
F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman · 2007
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An overview of the pets 2009 challenge
J. Ferryman and A. Shahrokni · 2009
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Image processing: Object segmentation using full-spectrum matching of albedo derived from colour images, 2010
M. Sedky, C. Chibelushi, and M. MONIRI · 2010
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Vibe: A universal background subtraction algorithm for video sequences
O. Barnich and M. Van Droogenbroeck · 2011
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Complementary background models for the detection of static and moving objects in crowded environments
R. H. Evangelio and T. Sikora · 2011
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Changedetection. net: A new change detection benchmark dataset
N. Goyette, P.-M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar · 2012
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Lecture 6a Overview of mini–batch gradient descent
G. Hinton, N. Srivastava, and K. Swersky · 2012
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Background segmentation with feedback: The pixel-based adaptive segmenter
Static and moving object detection using flux tensor with split gaussian models
R. Wang, F. Bunyak, G. Seetharaman, and K. Palaniappan · 2014
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Cdnet 2014: an expanded change detection benchmark dataset
Y. Wang, P.-M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar · 2014
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Learning sharable models for robust background subtraction
Y. Chen, J. Wang, and H. Lu · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Background subtraction for static & moving camera
H. Sajid and S.-C. S. Cheung · 2015
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A self-adjusting approach to change detection based on background word consensus
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M. Hofmann, P. Tiefenbacher, and G. Rigoll · 2012
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Change detection in feature space using local binary similarity patterns
G.-A. Bilodeau, J.-P. Jodoin, and N. Saunier · 2013
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BGSLibrary: An opencv c++ background subtraction library
A. Sobral · 2013
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Pid-based regulation of background dynamics for foreground segmentation
P. Tiefenbacher, M. Hofmann, D. Merget, and G. Rigoll · 2014
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P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin · 2015
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Subsense: A universal change detection method with local adaptive sensitivity
P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin · 2015
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Region-based mixture of gaussians modelling for foreground detection in dynamic scenes
S. Varadarajan, P. Miller, and H. Zhou · 2015
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Deep background subtraction with scene-specific convolutional neural networks
M. Braham and M. Van Droogenbroeck · 2016
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