Masked-RPCA: Moving Object Detection With an Overlaying Model

Masked-RPCA: Moving Object Detection With an Overlaying Model

Moving object detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance. Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition to accomplish such a task when the background is stationary and the foreground is...

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Bibliographic Details
Main Authors:	Amirhossein Khalilian-Gourtani, Shervin Minaee, Yao Wang
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Series:	IEEE Open Journal of Signal Processing
Subjects:	Moving object detection foreground-background subtraction nuclear-norm minimization low-rank matrices sparsity <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$\ell _0$</tex-math> </inline-formula>-pseudo-norm minimization
Online Access:	https://ieeexplore.ieee.org/document/9264716/

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