Online Robust Principal Component Analysis with Change Point Detection

TL;DR

This paper introduces OMWRPCA, an online robust PCA method capable of tracking both gradual and abrupt subspace changes, with embedded change point detection, suitable for real-time applications like video background subtraction.

Contribution

The paper presents a novel online robust PCA algorithm that effectively detects change points and adapts to both slow and sudden subspace changes, improving over existing batch methods.

Findings

OMWRPCA outperforms existing methods in simulations

It successfully detects change points in subspace

Effective for real-time video background subtraction

Abstract

Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this paper, we develop an efficient online robust principal component methods, namely online moving window robust principal component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can successfully track not only slowly changing subspace but also abruptly changed subspace. By embedding hypothesis testing into the algorithm, OMWRPCA can detect change points of the underlying subspaces. Extensive simulation studies demonstrate the superior performance of OMWRPCA compared with other state-of-art approaches. We also apply the algorithm for real-time background subtraction of surveillance video.