Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

Background modeling for foreground detection is often used in different applications to
model the background and then detect the moving objects in the scene like in video …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. A related easier problem is termed subspace learning or subspace estimation …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

We propose an effective online background subtraction method, which can be robustly
applied to practical videos that have variations in both foreground and background. Different …

Moving object detection is a fundamental step in various computer vision applications.
Robust principal component analysis (RPCA)-based methods have often been employed for …

Background modeling and foreground detection are important steps in video processing
used to detect robustly moving objects in challenging environments. This requires effective …

Background subtraction has been a fundamental and widely studied task in video analysis,
with a wide range of applications in video surveillance, teleconferencing, and 3D modeling …

Background estimation and foreground segmentation are important steps in many high-level
vision tasks. Many existing methods estimate background as a low-rank component and …