Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey

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Traditional and recent approaches in background modeling for foreground detection: An overview (2014, methodology): “This classification is better adapted to this field as shown in the survey [34] on recent advanced statistical background models.”
Foreground object detection in highly dynamic scenes using saliency (2014, methodology): “There have been many algorithms developed for foreground object detection [1, 2].”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “Moreover, we believe that we are living in a key transition in the field of background subtraction as we are progressively migrating from the conventional statistical models as MOG [357][33], KDE [95][30] and naive subspace learning models [29] to models based on robust decomposition into low-rank p”
CDnet 2014: An Expanded Change Detection Benchmark Dataset (2014, methodology): “We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.”
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos (2014, background): “Any reader could also refer to more complete states of the art in [3,7].”