A moving object detection method based on non-convex low-rank sparse decomposition

A technology of sparse decomposition and detection method, applied in the field of computer vision, can solve the problems affecting the extraction accuracy and low background accuracy, and achieve the effect of superior performance

Active Publication Date: 2022-07-29
NANJING UNIV OF POSTS & TELECOMM
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However, the nuclear norm is the sum of all non-zero singular values. Different sizes of singular values ​​have different effects on the nuclear norm. Therefore, the accuracy of the restored background is not high, which will affect the accuracy of the extraction of foreground moving targets to a certain extent. Spend

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  • A moving object detection method based on non-convex low-rank sparse decomposition
  • A moving object detection method based on non-convex low-rank sparse decomposition
  • A moving object detection method based on non-convex low-rank sparse decomposition

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[0054] Below in conjunction with specific embodiments, the present invention will be further illustrated, and it should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. The modifications all fall within the scope defined by the appended claims of this application.

[0055] Figure 2-Figure 7 This is a comparison diagram of the simulation results of the present invention based on the non-convex low-rank sparse decomposition model applied to the noise image corrupted by salt and pepper noise at a noise intensity of 0.05 and other models. figure 2 is the original image, image 3 is the noise image, Figure 4 to Figure 7 They are the images recovered by the algorithm proposed in the present invention, NNWNN (Nonconvex Nonsmooth Weighted Nuclear Norm), TNN (Truncation Nuclear Norm) and PCP algorithms.

[0056] The moving target detection method of the present invention focuses on solv...

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Abstract

The invention discloses a moving target detection method based on non-convex low-rank sparse decomposition, the steps are: 1. 0 The non-convex surrogate function in the general form of norm is applied to the approximation of low-rank matrices; and the non-convex surrogate function with good performance is selected; 2. The non-convex low-rank sparse decomposition model is established; 3. The singular value threshold SVT uses the generalized singular value The threshold GSVT is used instead; 4. Use the alternating direction multiplier method to solve the non-convex low-rank sparse decomposition model, and obtain the foreground target according to the obtained solution. The invention solves the problem that the approximate expression of the rank function in the existing method is inaccurate, resulting in that the recovered background is not clean enough and the accuracy of the extracted foreground target decreases.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to a moving target detection method based on non-convex low-rank sparse decomposition. Background technique [0002] Moving object detection is one of the most important and challenging tasks in the field of computer vision, and it is the basis of other computer tasks, such as object tracking, object recognition, etc. Therefore, moving object detection is particularly important. The background difference method is a common method for detecting moving objects. The performance of the background difference method mainly depends on the background modeling algorithm. Many models have been proposed, such as Gaussian Mixed Model (GMM) and visual background extractor (ViBe). But these models are based on a single pixel, so the association between pixels is usually ignored. [0003] In recent years, Low-Rank and Sparse Decomposition (LRSD), often also called Robust Principal Components Ana...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/40G06T7/20
CPCG06T7/20G06V20/42G06V20/40
Inventor 杨真真范露王鸿宇徐荣荣唐浪
Owner NANJING UNIV OF POSTS & TELECOMM
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