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 …

Computer vision applications based on videos often require the detection of moving objects
in their first step. Background subtraction is then applied in order to separate the background …

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 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 …

Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …

Background modeling is currently used to detect moving objects in video acquired from
static cameras. Numerous statistical methods have been developed over the recent years …

We describe an implementation of the Communication-Avoiding QR (CAQR) factorization
that runs entirely on a single graphics processor (GPU). We show that the reduction in …

Over the last decennium, the object detection is the pivotal step in any machine vision and
image processing application. It is the initial step applied to extract most informative pixel …

We propose the 3dSOBS+ algorithm, a newly designed approach for moving object
detection based on a neural background model automatically generated by a self …

Integrating image processing in video surveillance systems is a challenging task that has
been attempted for the past several decades. Despite being susceptible to crime, automated …