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 …

Change detection is one of the most important low-level tasks in video analytics. In 2012, we
introduced the changedetection. net (CDnet) benchmark, a video dataset devoted to the …

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 …

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 …

Convolutional neural network (CNN)-based systems are increasingly used in autonomous
vehicles for detecting obstacles. CNN-based object detection and per-pixel classification …

Background modeling has emerged as a popular foreground detection technique for various
applications in video surveillance. Background modeling methods have become increasing …

Fall event, as one of the greatest risks to the elderly, its detection has been a hot research
issue in the solitary scene in recent years. Nevertheless, most current researches are …

In this paper, we present a weighted tensor Schatten-p quasi-norm (0< p< 1) regularizer for
3D array datasets in order to recover the low-rank part and the sparse part, respectively …