Understanding Background Mixture Models for Foreground Segmentation

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Visual Tracking: an Experimental Survey (2013, unknown): “For static scenes, background intensity representation is an old solution [84], [85] only suited for standardized circumstances, improved with background intensity prediction with simple statistics [86], [87], [88].”
Efficient adaptive density estimation per image pixel for the task of background subtraction (2006, unknown): “In (Power and Schoonees, 2002) the GMM is extended with a hysteresis threshold.”
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey (2008, methodology): “Following the Bayes criterion, a pixel is assigned to be background when: where the”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, methodology): “proach [66], [187], [188], [162], principal component analysis (PCA) [157], [161], [162], robust PCA [158], [189], independent component analysis [159], [190], and discriminant center surround [151], [160].”
ViBe: A Universal Background Subtraction Algorithm for Video Sequences (2011, methodology): “Since its introduction, the model has gained vastly in popularity among the computer vision community [4], [7], [11], [42]–[44], and it is still raising a lot of interest as authors continue to revisit the method and propose enhanced algorithms [45]–[50].”