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 …

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 …

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 …

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 …

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 …

This book provides a complete overview of the concepts, algorithms, and applications
related to background modeling and foreground detection. It presents the top methods and …

Video background modeling is an important preprocessing step in many video analysis
systems. Principal component pursuit (PCP), which is currently considered to be the state-of …

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image
and Video Processing shows you how robust subspace learning and tracking by …

Moving object detection is a fundamental task in many video processing applications, such
as video surveillance. The robustness and efficiency of background subtraction make it one …

Extracting moving objects from a video sequence and estimating the background of each
individual image are fundamental issues in many practical applications such as visual …