Monitoring video data quality evaluation method and apparatus based on edge computing model

A technology for monitoring video and data quality, applied in television, closed-circuit television systems, electrical components, etc., can solve problems such as inability to accurately judge the quality of video surveillance data, reduce the average repair time, ensure quality, and solve the problem of inconsistent subjective standards Effect

Inactive Publication Date: 2017-09-22
ANHUI UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to solve the above-mentioned technical defects that the video surveillance system cannot accurately judge the quality of video

Method used

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  • Monitoring video data quality evaluation method and apparatus based on edge computing model
  • Monitoring video data quality evaluation method and apparatus based on edge computing model
  • Monitoring video data quality evaluation method and apparatus based on edge computing model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] The present invention is applicable to a video monitoring system, and the video monitoring system mainly includes three parts: cloud, edge terminal and client. This embodiment can be developed based on Win7x64, VS2015, opencv3.2.0 environment.

[0039] The recommended hardware environment is InterCore i5-6500@3.20GHz quad-core processor, 16GB (Kingston DDR42400MHz) memory, and InterHD Graphics530 graphics card.

[0040] The above software and hardware environments are only for better illustration of the present invention, and are only for illustration, and do not limit the protection scope of the present invention. Those skilled in the art can choose other suitable software and hardware environments according to actual needs.

[0041] Such as figure 1 As shown, this embodiment provides a method for evaluating the quality of video surveillance data based on an edge computing model, including the following steps:

[0042] Video acquisition step S1: periodically acquire ...

Embodiment 2

[0104] Such as Figure 13 As shown, the difference between this embodiment and embodiment 1 is that an offline monitoring step S0 is added before the video acquisition step S1: judging whether the video monitoring terminal is offline according to the ping command.

[0105] The specific operation of the ping command is to ping the IP address of the video surveillance terminal to obtain the packet loss rate, and check the packet loss rate in the result to determine whether the video terminal is offline. Generally, when the packet loss rate is greater than 100%, it is judged that the video monitoring terminal is in an offline state.

[0106] Other technical features of this embodiment are the same as those of Embodiment 1, and will not be repeated here.

[0107] According to the above description, it can be seen that the evaluation method of video surveillance data quality based on the edge computing model provided by this embodiment can effectively monitor the online status of ...

Embodiment 3

[0109] Such as Figure 14 As shown, a surveillance video data quality evaluation device based on an edge computing model, including:

[0110] Video acquisition unit 1: used to periodically acquire a piece of video from the video monitoring terminal according to a preset time interval. Data processing unit 2: for processing the collected video and extracting the corresponding background picture.

[0111] Quality detection unit 3 : including a blur detection module 31 and / or a color detection module 32 .

[0112] in,

[0113] The blur detection module 31 is used to analyze and calculate the extracted background picture to obtain the corresponding blur value; and judge according to the preset blur threshold, if the blur value is less than the blur threshold, it is judged as image blur; if the blur value If it is greater than or equal to the blur threshold, the image is judged to be clear.

[0114]The color detection module 32 is used to analyze and calculate the extracted bac...

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Abstract

The invention provides a surveillance video data quality evaluation method based on an edge computing model, which is characterized in that it comprises the following steps: a video acquisition step, a data processing step and a quality detection step. The quality detection step includes a blur detection step and/or a color detection step; wherein, the blur detection step analyzes and calculates the extracted monitoring video background picture to obtain a corresponding blur value; and judges whether the image is clear according to a preset blur threshold; the color The detection step analyzes and calculates the extracted background image according to the color histogram algorithm to obtain the color ratio; and judges whether the image color is normal according to the preset color threshold. The present invention can accurately judge the quality of video surveillance images, and fundamentally solve the technical defects of time-consuming and labor-intensive manual judgment of video surveillance image quality and inconsistent subjective standards. The present invention can further intelligently identify offline network faults and improve fault detection efficiency.

Description

technical field [0001] The invention belongs to the technical field of video monitoring, and in particular relates to a monitoring video data quality evaluation method and device based on an edge computing model Background technique [0002] With the expansion of city scale, the role of video surveillance systems in public security, financial securities, banks, stores, intelligent buildings, etc. has become increasingly prominent, especially in public security. Nowadays, the overall scope of the monitoring system is constantly expanding, and the monitoring density is also expanding. In video monitoring terminals with large data scale, we may not be able to accurately locate and process all fault information for monitoring terminal equipment errors, so a certain amount of manpower is required to check these fault information, and in the process, terminal users pass Judging the content of the video based on the user experience, the cost of manpower and material resources is a...

Claims

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Application Information

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IPC IPC(8): H04N17/00H04N7/18
CPCH04N17/00H04N7/18
Inventor 孙辉梁旭施巍松仲红
Owner ANHUI UNIVERSITY
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