Video foreground separation method and system based on adaptive robust principal component analysis

Abstract

The invention relates to a video foreground separation method and system based on adaptive robust principal component analysis. The method comprises the following steps: firstly, each image in an image sequence is pulled into a column vector according to a row, and then the column vector is combined into a new matrix M. The matrix M is decomposed by singular value decomposition, and the rth singular value is used as the initial threshold of singular value threshold operation. Then, the first r singular values are reconstructed to form a new matrix Mr, and the ratio of the information containedin the matrix Mr reconstructed by each singular value to the information contained in the matrix Mr is calculated. The singular values are adaptively sparsed according to their proportional magnitude. Finally, according to the inexact augmented Lagrange multiplier method, the matrix M is decomposed into a low rank matrix and a sparse matrix by the singular value threshold operation model. Experiments show that the method of the invention takes into account the influence of information fusion between the foreground target 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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