Surveillance Video Processing Using Compressive Sensing

TL;DR

This paper introduces a compressive sensing approach that decomposes surveillance video matrices into low rank and sparse parts to effectively segment backgrounds and detect moving objects using fewer measurements.

Contribution

It presents a novel method combining compressive sensing with matrix decomposition for efficient surveillance video analysis.

Findings

Moving objects are reliably extracted with fewer measurements.

The method effectively separates background and foreground in surveillance videos.

Experiments demonstrate robustness and efficiency of the approach.

Abstract

A compressive sensing method combined with decomposition of a matrix formed with image frames of a surveillance video into low rank and sparse matrices is proposed to segment the background and extract moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to reconstruct the video by a low rank and sparse decomposition of matrix. The low rank component represents the background, and the sparse component is used to identify moving objects in the surveillance video. The decomposition is performed by an augmented Lagrangian alternating direction method. Experiments are carried out to demonstrate that moving objects can be reliably extracted with a small amount of measurements.