Compressive Sensing for Background Subtraction

By Volkan Cevher, Aswin C. Sankaranarayanan, Marco F. Duarte, Dikpal Reddy, Richard Baraniuk, Ramalingam Chellappa. European Conference on Computer Vision, 2008

347 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/935df6dd601b7569bc50905f1c1085afe2c4bbca

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In Biomedical Engineering

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “Examples of solvers include Basis Pursuit (BP) [64][76], Basis Pursuit Denoising (BPDN) [44][51], Orthogonal Matching Pursuit (OMP) [76], Stagewise OMP (StOMP)[84][412], Lattice Matching Pursuit (LaMP) [50], Convex Lattice Matching Pursuit (CoLaMP) [322], Compressive sampling matching pursuit (CoSaM”
Robust Visual Tracking using 1 Minimization (2009, background): “Other applications include background subtraction [6], media recovery [15], texture segmentation [24], and lighting estimation [26], etc.”
Real-time visual tracking using compressive sensing (2011, methodology): “CS has been applied to various computer vision tasks [6, 9, 10].”
Modified-CS: Modifying compressive sensing for problems with partially known support (2009, methodology): “ not to improve reconstruction error). Except [14], none of these prove exact reconstruction using fewer measurements and except [15], [14], none of these even demonstrate it. Other recent work, e.g. [18], applies CS on observation differences to reconstruct the difference signal. While their goal i”
Robust visual tracking using ℓ1 minimization (2009, background): “Other applications include background subtraction [6], media recovery [15], texture segmentation [24], and lighting estimation [26] , etc.”
Sparse Representation for Computer Vision and Pattern Recognition (2010, methodology): “…variations and extensions of 1 minimization have been applied to many vision tasks, including face recognition [71], image super-resolution [75], motion and data segmentation [33], [56], supervised denoising and inpainting [51] and background modeling [16], [21] and image classification [47], [48].”
Compressive sensing: From theory to applications, a survey (2013, background): “CS solves solves the problem by making use of the fact that in vision applications, natural images can be sparsely represented in wavelet domains [59].”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, neutral): “age reconstruction problem [145], [166], [167], [168], [169].”
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images (2014, positive): “Sparse representations have also been applied to face recognition [26], image background modeling [3], and image classification [20].”
Robust Visual Tracking and Vehicle Classification via Sparse Representation (2011, background): “Within the computer vision community, compressive sensing has been applied to a variety of problems such as face recognition [43], background subtraction [9], [20], media recovery [16], texture segmentation [31], lighting estimation [33], and so on.”