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 …

Several methods have been proposed to solve moving objects segmentation problem
accurately in different scenes. However, many of them lack the ability of handling various …

With the increasing number of machine learning methods used for segmenting images and
analyzing videos, there has been a growing need for large datasets with pixel accurate …

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 …

Despite the promising results, tensor robust principal component analysis (TRPCA), which
aims to recover underlying low-rank structure of clean tensor data corrupted with …

Foreground segmentation algorithms aim at segmenting moving objects from the
background in a robust way under various challenging scenarios. Encoder–decoder-type …

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 …

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 subtraction (BGS) is a fundamental video processing task which is a key
component of many applications. Deep learning-based supervised algorithms achieve very …