Video Foreground Separation Method and System Based on Adaptive Robust Principal Component Analysis

Abstract

The present invention relates to a video foreground separation method and system based on adaptive robust principal component analysis, comprising: first pulling each image in the image sequence into column vectors by row, and then combining the column vectors into a new matrix M; Singular value decomposition is performed on the matrix M, and the rth singular value is used as the initial threshold of the singular value threshold operation; then the first r singular values ​​are reconstructed to form a new matrix M r , and calculate the information contained in the matrix formed by the individual reconstruction of each singular value to account for the matrix M r The scale of the information contained; the singular values ​​are adaptively thinned according to the scale. Finally, according to the inexact augmented Lagrangian multiplier method, the matrix M is decomposed into a low-rank matrix and a sparse matrix through the singular value threshold operation model. Experiments show that the method of the present invention takes into account the influence of information fusion between the foreground object and the background, and accurately separates the low-rank background part and the sparse foreground part.

Description

technical field [0001] The invention belongs to the field of video image processing, and relates to a video foreground separation method based on adaptive robust principal component analysis, in particular to a method for separating the foreground and background of a monitoring image under a static camera. Background technique [0002] With the rapid development of network technology and digital video technology, surveillance technology is increasingly oriented toward intelligence and networking, which makes the requirements for background extraction technology of surveillance images higher and higher. Background extraction is often used to segment dynamic objects from a scene captured by a static camera. Typical methods include: basic background modeling, background estimation, fuzzy background modeling and statistical background modeling. The basic idea of ​​these traditional methods is to first extract the background features of the image sequence by learning a training i...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the problem of poor effect of the low-rank background recovered by RPCA, and propose an adaptive robust principal component analysis method

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