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Video sequence background recovery method based on online low-rank background modeling

A technology for video sequence and background modeling, applied in the field of video analysis

Active Publication Date: 2016-12-07
TIANJIN UNIV
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Problems solved by technology

The technical scheme adopted by the present invention is based on the video sequence background recovery method of online low-rank background modeling, and introduces low-rank matrix decomposition of nuclear norm and binarized motion information estimation on the basis of traditional background modeling, thereby solving the problem of Problems that existing technologies cannot handle

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  • Video sequence background recovery method based on online low-rank background modeling
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  • Video sequence background recovery method based on online low-rank background modeling

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Embodiment Construction

[0071] The low-rank decomposition of nuclear norm and motion information estimation are introduced into the traditional matrix low-rank sparse decomposition model, so that the accurate background of the video sequence can be recovered online, that is, the video sequence background recovery method based on online low-rank background modeling, Thereby solving the problem that the prior art cannot handle. The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0072] 1) The background restoration problem in the video sequence is specifically expressed as solving the following unconstrained optimization equation:

[0073]

[0074] Where||·|| F Represents the Frobenius norm of a matrix. ||·|| * Represents the kernel norm of a matrix. ||·|| 1 Indicates the one-norm of the matrix. ο represents the dot product operation of two matrices. D is a matrix in which actual video sequence frames are arranged sequential...

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Abstract

The invention belongs to the video analysis field and aims at accurately recovering a video sequence background in an online mode. The invention discloses a video sequence background recovery method based on online low-rank background modeling. Based on traditional background modeling, low-rank matrix decomposition of a nuclear norm and motion information estimation of binaryzation are introduced so that a problem which can not be processed in the prior art can be solved. The method comprises the following steps of 1) specifically expressing a background recovery problem in a video sequence as following unrestraint optimization equation solving; 2) constructing a kth frame of motion mapping weight vector wk; 3) using an alternative direction method to solve; 4) solving ck<l+1>; 5) solving ek<l+1>; 6) repeating the above steps 4) and 5) till that algorithm convergence is achieved; 7) using variable substitution to solve; 8) solving Yk<l+1>; 9) solving Lk<l+1>; 10) repeating the above steps 8) and 9) till that the algorithm convergence is achieved; and 11) solving a video background. The method of the invention is mainly used for a video analysis occasion.

Description

technical field [0001] The invention belongs to the field of video analysis. In particular, it relates to video sequence background restoration methods based on online low-rank background modeling. Background technique [0002] With the rapid development of the Internet and mass media, the availability of video data has increased rapidly, far beyond the scope of human manual analysis. Therefore, it is of great significance to use automatic video analysis to mine interesting information in a large number of videos. Background restoration, as a preprocessing technique for extracting objects of interest in videos, is widely used in many vision applications, such as object detection, object tracking, and action recognition. [0003] In recent years, there have been many research results on the method of solving the problem of video background restoration. The most important method is the method based on Robust Principal Component Analysis (RPCA). The general idea is: Assume th...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/10016G06T5/73
Inventor 杨敬钰杨蕉如杨雪梦
Owner TIANJIN UNIV
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