Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance

By Thierry Bouwmans, El-Hadi Zahzah. Computer Vision and Image Understanding, 2014

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Traditional and recent approaches in background modeling for foreground detection: An overview (2014, background): “, PCA-l1 [152], Half-Quadratic-PCA (HQ-PCA) [121] Pursuing Dynamic Spatio-Temporal Models (STDM) [163] ))(Survey (Bouwmans and Zahzah (2014) [40] , SPCP [328], QPCP [23], BPCP [252], LPCP [288] , SOP [295] , SpaCorr [127] , Spatial IRLS [109] , Spatio-temporal IRLS [109] , Variational Bayesian RPCA…”
Fast Robust PCA on Graphs (2015, methodology): “he unavailability of low-rank ground truth for these videos we do not present any quantitative results. However, in future we will work on foreground extraction and present quantitative results as in [5]. 8https://vid.me/ixS3 8https://vid.me/UtDZ 10https://vid.me/Ad1w 14 D. Computational Time TABLE ”
Bounding Box Regression With Uncertainty for Accurate Object Detection (2018, background): “bject detection is fundamental for many downstream practical computer vision applications like face recognition [59,53], autonomous driving cars [10,31], video indexing [51,41] and video surveillance [2,37]. Recent advances on deep convolutional neural network greatly improve the performance of obje”
Robust Principal Component Analysis on Graphs (2015, methodology): “We will also work on foreground extraction and present quantitative results as in [3].”
Principal component analysis: a review and recent developments (2016, background): “A fairly recent review of work in this area can be found in [46].”