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 …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. A related easier problem is termed subspace learning or subspace estimation …

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

In this article, we propose a novel algorithm, namely PETRELS-ADMM, to deal with
subspace tracking in the presence of outliers and missing data. The proposed approach …

Online robust principal component analysis (RPCA) algorithms recursively decompose
incoming data into low-rank and sparse components. However, they operate on data vectors …

Recovering the low-rank and sparse components from a given matrix is a challenging
problem that has many real applications. This paper proposes a novel algorithm to address …

With the increased proliferation of smart devices, the transit passengers of today expect a
higher quality of service in the form of real-time traffic updates, accurate expected time-of …

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 …

We study the problem of subspace tracking in the presence of missing data (ST-miss). In
recent work, we studied a related problem called robust ST. In this paper, we show that a …