Separation method of foreground and background of video based on low rank and structure sparseness

Abstract

The present invention relates to a separation method of the foreground and the background of a video based on low rank and structure sparseness. The method comprises: the step 1, reading a video sequence; the step 2, performing low-rank and structure sparse matrix decomposition through adoption of a low-rank and structure sparse non-precision lagrangian multiplier method; the step 3, estimating the rank r of a background matrix A in advance; the step 4, reconstructing the background matrix A, namely, performing decomposition through a singular value, and calculating the background matrix A by using the rank r and a singular value contraction operator; and the step 5, reconstructing a foreground matrix E, obtaining the background matrix A through an observation matrix D and the step 4, and calculating a foreground matrix E; the step 6, determining the iteration times and the errors, and if the iteration times are equal to or smaller than iteration errors, the iteration is finished; and the step 7, recovering the background and the foreground images. The separation method of the foreground and the background of a video based on low rank and structure sparseness is able to directly realize the separation of the background and the foreground of the video sequence.

Description

technical field [0001] The invention relates to the background extraction technology, in particular to the monitoring video background and foreground separation technology under a static camera. Background technique [0002] A good foreground and background separation scheme is one of the key technologies to realize moving target detection and recognition. 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 [1] . The basic idea of ​​these methods is to first extract the background features of the video by learning a training image sequence, thereby establishing a mathematical model to describe its background, and then use the background model to process the video sequence to be detected (generally using the background subtraction method). ), extract the pixels in the current image that ...

AI Technical Summary

Problems solved by technology

[0003] The sparsity of the signal is not the only signal representation model, and the low-rank matrix decomposition is not perfect in theory, as the nuclear norm cannot completely and correctly approximate the rank function of the matrix [3] , the signal itself still has some structural prior information that has not been fully mined [4]

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