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Effective enrichment method for monitor video under condition of leaf disturbance

A technology for monitoring video and leaves, applied in color TV parts, TV system parts, TV and other directions, can solve problems such as affecting the concentration accuracy rate and leaf disturbance.

Active Publication Date: 2018-02-02
ANHUI UNIVERSITY
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  • Abstract
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AI Technical Summary

Problems solved by technology

Street surveillance video, because of the green belts on both sides, leaf disturbance is common, which seriously affects the concentration accuracy

Method used

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  • Effective enrichment method for monitor video under condition of leaf disturbance
  • Effective enrichment method for monitor video under condition of leaf disturbance
  • Effective enrichment method for monitor video under condition of leaf disturbance

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Embodiment Construction

[0060] The present invention is further described below in conjunction with specific embodiment, but protection scope of the present invention is not limited thereto:

[0061] Embodiment For example, as shown in the accompanying drawings, an effective method for concentrating surveillance video under the condition of leaf disturbance, it includes the following steps:

[0062] (1) Background modeling and foreground extraction:

[0063] For each frame of the input video, the background modeling based on the mixed Gaussian model is performed to separate the regions of the foreground object and the background object.

[0064] For the multi-peak Gaussian distribution model, each pixel of the image is modeled by the superposition of multiple Gaussian distributions with different weights. Each Gaussian distribution corresponds to a state that may produce the color of the pixel. The weight of each Gaussian distribution and distribution parameters are updated over time.

[0065] When...

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Abstract

The invention provides an effective enrichment method for a monitor video under a condition of leaf disturbance. The method mainly comprises the following steps of performing mixed Gaussian model-based background modeling on an input video, acquiring a background model, and separating moving objects; eliminating, through erosion and expansion operations, some noise of each frame after mixed Gaussian modeling; preliminarily determining whether to keep a current frame according to a proportion of a foreground object to a whole image; partitioning a background image and a current frame image, computing and comparing difference of color histograms of each block of the background frame and current frame images, and determining whether the moving object is leaf disturbance or a foreground target; and keeping foreground frames that are not leaf disturbance, and combining the frames so as to generate an enriched video. The method has the advantage that a policy based on block histogram comparison is provided for the monitor video under the condition of leaf disturbance, thereby effectively improving video enrichment robustness and accuracy.

Description

technical field [0001] The invention relates to the fields of pattern recognition and image processing, in particular to an effective concentration method for monitoring video under the condition of leaf disturbance. technical background [0002] With the rapid advancement of safe cities and intelligent transportation, intelligent, high-definition, and networked digital video surveillance has received unprecedented attention. The condensed summary of massive video information and system decisions based on video content analysis will become unstoppable in the video surveillance industry. development trend. How to browse video information in massive video databases in a short period of time and quickly retrieve the required video data has become the most important research content in the current video field, especially in traffic and security video applications. For example, in the major armed robbery case in Nanjing on January 6, 2012, the public security department spent a ...

Claims

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Application Information

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IPC IPC(8): H04N5/14H04N7/18H04N21/8549
CPCH04N5/144H04N5/147H04N7/18H04N21/8549
Inventor 孙战里沈韬
Owner ANHUI UNIVERSITY
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