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 …

Visual change detection, aiming at segmentation of video frames into foreground and
background regions, is one of the elementary tasks in computer vision and video analytics …

During about 30 years, a lot of research teams have worked on the big challenge of
detection of moving objects in various challenging environments. First applications concern …

In the computer vision, background extraction is a promising technique. It is characterized by
being applied in many different real time applications in diverse environments and with …

Mathematical tools, machine learning, and signal processing tools have achieved enormous
success in computer vision. In this chapter, we present state-of-the-art progress that has …

Society 5.0 is based on environmental sensor information, equipment operating status,
people related information, etc. A part of this information comes from video surveillance …

Detection of moving objects is often the first stage in many computer vision applications
through the use of static or moving cameras. Subsequently, the separation process between …

This paper extends the subspace learning method Robust principal component analysis
(RPCA) for background modeling to recover the background scene from video sequence …