A camera resource scheduling method and device in dynamic tracking

Abstract

The invention discloses a camera resource scheduling method and device in dynamic tracking. The method includes: creating video analysis tasks of N initial cameras to implement monitoring tasks on targets, and determining the camera where the target is located from the initial N cameras ; According to the video analysis results of the N initial cameras obtained, calculate the target pixel coordinate position of the camera where the target is located, and calculate the spatial coordinate position of the target according to the target pixel coordinate position; query the background database, according to the spatial coordinate position of the target, Find all the associated cameras according to the preset spatial association range, and dispatch the associated cameras to monitor the target. The invention can combine background data management technology and video positioning technology to carry out reasonable and accurate task scheduling for cameras, reduce unnecessary video analysis, thereby greatly saving the cost of dynamic tracking technology and ensuring full and effective use of resources.

Description

technical field [0001] The invention relates to the technical field of video monitoring, in particular to a camera resource scheduling method and device in dynamic tracking. Background technique [0002] At present, machine vision has been widely used in video surveillance, public security and fire protection industries, and target tracking is one of the typical applications. The deep learning algorithm is used to analyze the monitoring screen of the real-time camera, and the characteristics of the analysis result are compared with the characteristics of the tracking target. If the comparison result exceeds the preset threshold, the target is considered to be the tracking target. If all the cameras in the monitoring area are visually analyzed, and the spatial positions of the cameras are known, as long as you know which camera captured the target, you can know the current spatial position of the target, so as to achieve the purpose of target tracking. [0003] Visual analys...

AI Technical Summary

Problems solved by technology

[0004] However, the cost of the commonly used solutions in the prior art is relatively high. The face dynamic analysis server (analyzes the faces in the real-time video screen and performs target comparison) costs about 200,000 to 300,000 in 28 channels. In the case of the road, the cost of the face server alone can reach millions. If the human body structure analysis server is added, the cost is very expensive

In addition, in the actual tracking situation, if a camera that is far away from the target or a camera outside the field of view is called for analysis tasks, it will cause a waste of resources

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