Video quality diagnosis control system and method applied to video monitoring network

Abstract

The invention relates to a video quality diagnosis control system and method applied to a video monitoring network, belonging to the technical field of network video. The video quality diagnosis control system comprises a task management server, a video accessing and polling server, a diagnosis method server and a diagnosis result management server. The video quality diagnosis control method comprises the following steps: the task management server sends a diagnosis task to the video accessing and polling server and the diagnosis method server, and sends a diagnosis method parameter to the diagnosis method server; the video accessing and polling server obtain image information of all video sources from the video monitoring network and sends the image information to the diagnosis method server; and the diagnosis method server determines a diagnosis method according to the diagnosis task and the diagnosis method parameter, diagnoses the image information of all video sources, generates a diagnosis result and sends the diagnosis result to the diagnosis result management server. According to the method and the system provided by the invention, video contents of a network front end can be automatically analyzed, and video failures are diagnosed; therefore, the diagnosis accurate rate and the reaction speed are greatly increased and the false positive rate is lowered.

Description

technical field [0001] The invention relates to the technical field of network video, in particular to the technical field of network video quality detection, in particular to a video quality diagnosis control system and method applied to a video monitoring network. Background technique [0002] The traditional technology of video fault inspection using surveillance cameras is almost entirely dependent on manpower. For example, on a network monitoring platform with a scale of 10,000, it takes an average of 1 minute per person to transfer, detect, and judge a camera, and it takes a person almost 167 hours a week (roughly 21 working days) to complete the detection, which fully exposes the manual method. Problems: 1) The efficiency of the manual method is low; 2) The diagnosis speed is slow, which increases the risk of public safety; because the faulty camera cannot effectively record the event information of the scene. Therefore, in the face of the rapid development of public...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the above-mentioned shortcomings in the prior art, and provide a method of using image processing and pattern recognition technology to automatically process and analyze the content of the front-end video in the video surveillance network, detect and diagnose video faults, thereby solving the low efficiency of manual detection , the problem of slow response, and

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