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A Background Modeling Method Based on Sample Local Density Outlier Detection

A technology of outlier detection and local density, applied in image analysis, instrumentation, calculation, etc., can solve problems such as struggle and achieve the effect of increasing authenticity

Inactive Publication Date: 2018-05-01
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

These methods can achieve very good results when dealing with static backgrounds, but when faced with multi-modal dynamic backgrounds, they often struggle.

Method used

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  • A Background Modeling Method Based on Sample Local Density Outlier Detection
  • A Background Modeling Method Based on Sample Local Density Outlier Detection
  • A Background Modeling Method Based on Sample Local Density Outlier Detection

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] Such as figure 1 As shown, the background modeling method based on sample local density outlier detection of the present invention comprises the following steps:

[0050] Step 1: Initialize the background model by using the video frames that have been observed in the previous N frames, so that each pixel point initializes a sample set, and calculate the local background factor of each sample point in the sample set.

[0051] Step 2: For each new observed pixel value, calculate its local background factor.

[0052] Step 3: Compare the local background factor of the newly observed pixel value with its nearest neighbor sample points to determine whether it belongs to the background.

[0053] Step 4: If the new pixel value is determined to belong to the background, update the background model. Incorporate new pixel values, and replace the sample p...

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Abstract

The invention discloses a background modeling method based on sample local density outlier detection. Calculate the local background factor of the observed new pixel value; compare the local background factor of the newly observed new pixel value with its nearest neighbor sample points to determine whether it belongs to the background, and if it belongs to the background, integrate the new pixel value into The background model is updated, and the sample point with the largest local background factor in the sample set is replaced by the new pixel value; the present invention uses the observed pixel real value to initialize the background model, which increases the authenticity of the background model and avoids being incorrectly The presence of false values ​​affects the possibility.

Description

technical field [0001] The invention relates to a background modeling method based on sample local density outlier detection. Background technique [0002] Background modeling methods are very basic and critical for many applications in the field of computer vision. With the rapid development of computer vision, the requirements for background modeling effects are getting higher and higher. Although many excellent background modeling methods have emerged at this stage, there is still a certain gap from people's requirements, especially in the face of dynamic backgrounds, most of the existing algorithms cannot complete background modeling well. task. [0003] Most of the current background modeling methods use parametric models to model each pixel, that is, methods based on parametric models, while others directly use observed actual pixel values ​​for modeling, that is, The method based on sample points. These methods can achieve very good results when dealing with static...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/136
Inventor 杨明强曾威崔振兴
Owner SHANDONG UNIV
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