Independent Component Analysis-Based Background Subtraction for Indoor Surveillance

By Du-Ming Tsai, Shia-Chih Lai. IEEE Transactions on Image Processing, 2009

322 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/63e320efa7a82bc47ad802e44e99aac8c6090e31

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Citations in the corpus

ViBe: A Universal Background Subtraction Algorithm for Video Sequences (2011, methodology): “A similar approach, the independent component analysis (ICA) of serialized images from a training sequence, is described in [13] in the training of an ICA model.”
A survey on activity recognition and behavior understanding in video surveillance (2013, background): “[185] independent component analysis i g s s computational fast, significant illumination changes”
Maximum Margin Criterion (2018, methodology): “[15] proposed to use the Independent Component Analysis (ICA) which is a variant of PCA in which the components are assumed to be mutually statistically independent instead of merely uncorrelated.”
SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity (2015, background): “rely on region-level [27], frame-level [28], [29] or hybrid frame/region-level [30], [31] comparisons to explicitly model the spatial dependencies of neighboring pixels.”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, background): “Background Initialization Background Maintenance Foreground Detection SG [13] MOG [14] KDE [15] SL-PCA [165] 1) O(P) SVM [180] SVR [183] SVDD [189] O(1) O(1) O(1) SGG [190] MOGG [194] SL-ICA [200] SL-INMF [202] SL-IRT [205]”
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering (2012, background): “In the last decades, it has attracted widespread interest due to a large number of applications in diverse disciplines such as remote sensing [2]–[10], medical diagnosis [11], [12], and video surveillance [13], [14].”
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].”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, methodology): “[270] used an Independent Component Analysis (ICA).”