A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos

By Andrews Cordolino Sobral, Antoine Vacavant. Computer Vision and Image Understanding, 2014

688 citations recorded by the source corpus.

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Improving RGB-D SLAM in dynamic environments: A motion removal approach (2017, methodology): “Classical motion segmentation methods, such as background subtraction [17], become virtually useless when camera is not static.”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, background): “• BMC 2012 Dataset: The BMC30 (Background Models Challenge) [275][242] is a workshop organized within ACCV (Asian Conference in Computer Vision) about the comparison of background subtraction techniques with both synthetic and real videos.”
Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, methodology): “They have similarities to background subtraction in image processing [166].”
Camera-trap images segmentation using multi-layer robust principal component analysis (2017, methodology): “Sobral andVacavant [32] compare 29 stateof-the-art background subtraction methods using the BGS Library [29].”