Online Robust Background Modeling via Alternating Grassmannian Optimization

Low-rank subspaces have long been a powerful tool in data modeling and analysis. In particular, they have proven very useful in computer vision: Subspace models are of great interest in computer vision for background subtraction [52], object tracking [5, 19], and to represent a single scene under va...

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Bibliographic Details
Published inBackground Modeling and Foreground Detection for Video Surveillance pp. 401 - 426
Format Book Chapter
LanguageEnglish
Published Chapman and Hall/CRC 2015
Subjects
Virtual Camera
Camera Jitter
Images Aligned
LRT
Robust PCA
Unstable Camera
ADMM Algorithm
Dynamic Background
Video Frames
Online Algorithm
Subspace Models
Image Ii
Subspace Tracking
Alternating Direction Method
Subspace Learning
Background Separation
Foreground Separation
Grassmannian Manifold
Dual Vector
Low Dimensional Subspace
Low Rank Model
Background Subtraction
Low Rank Matrix
Original Video Frame
Virtual Pan
Online AccessGet full text
DOI10.1201/b17223-24

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Summary:Low-rank subspaces have long been a powerful tool in data modeling and analysis. In particular, they have proven very useful in computer vision: Subspace models are of great interest in computer vision for background subtraction [52], object tracking [5, 19], and to represent a single scene under varying illuminations [9, 20]. Other applications in communications [40], source localization and target tracking in radar and sonar [33], and medical imaging [3] all leverage subspace models in order to recover the signal of interest and reject noise. In these classical signal processing problems, a handful of high-quality sensors are co-located such that data can be reliably collected.
DOI:10.1201/b17223-24