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Sparse coding background modeling method

A sparse coding and background modeling technology, applied in image data processing, instrumentation, computing, etc., can solve the problem that the global model cannot distinguish moving objects, and achieve the effect of improving foreground detection accuracy, small impact, and accurate detection.

Inactive Publication Date: 2014-04-23
DALIAN UNIV OF TECH
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Problems solved by technology

This method of directly modeling the global background is sensitive to strong local background changes, but the frame-level global model cannot distinguish between moving objects in the background and moving objects in the foreground

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Embodiment Construction

[0031] In order to make the technical solutions and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the drawings in the embodiments of the present invention:

[0032] Such as figure 1 As shown, the background modeling method disclosed in the present invention uses a discriminative atomic model method to distinguish background pixels and foreground pixels of an image. The discriminative atomic model method adopts the following method: firstly, the image is divided into multiple image blocks, Based on the image sparse coding model, the average information content and word frequency-inverse document frequency (tf-idf) technology are used to statistically analyze the atoms in the sparse coding dictionary, find out the atoms carrying discriminative information, and use the discriminative Atom reconstruction image background information.

[0033] The sp...

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Abstract

The invention discloses a sparse coding background modeling method. The method adopts a discriminative atom model method for carrying out background modeling on images, and comprises the following steps that 1, collected images are divided into a plurality of image blocks, and the image blocks are subjected to sparse coding; 2, on the basis of the sparse coding model of the images, discriminative atoms are found out from atoms in a sparse coding dictionary by using a frequency-inverse file frequency (tf-idf) statistics analysis method; 3, the selected discriminative atoms are used for completing the image background rebuilding. The technical scheme is adopted, so the image background modeling method provided by the invention has the advantages that the average information quantity and frequency-inverse file frequency (tf-idf ) technology is utilized for carrying out statistics analysis on the atoms in the sparse coding dictionary on the basis of the image sparse coding model, the atoms carrying the discriminative information, i.e. the discriminative atoms are found out, and the discriminative atoms are used for rebuilding the image background information.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a sparse coding background modeling method. Background technique [0002] Background extraction and background removal are key steps in image processing in the technical field of image processing. Background modeling methods in the prior art are mainly divided into pixel-level methods and frame-level methods. Typical pixel-level methods include: frame difference method [1], kernel density method [3] and mixed Gaussian method [2]. The frame difference method uses the difference between adjacent frames to detect moving objects. This method has a simple algorithm and fast calculation speed, but it is not accurate enough for foreground detection. Especially when the foreground area is large, the pixel brightness distribution is relatively uniform, and the motion speed is slow, there will be large holes in the detection of the foreground by the frame difference method. The ...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 戚金清胡阳
Owner DALIAN UNIV OF TECH
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