Reasoning resource allocation optimization method and system for multi-camera real-time video monitoring

By dynamically adjusting the sampling interval and computing resource allocation of the camera in the multi-camera video surveillance system, the problem of low resource utilization in the existing technology is solved, efficient detection and response to abnormal events under limited resources is achieved, and the overall efficiency of the system is improved.

CN120492149APending Publication Date: 2025-08-15SOUTHWEAT UNIV OF SCI & TECH +1
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Patent Information

Application Number
CN202510555086.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2025-08-15

AI Technical Summary

Technical Problem

The existing multi-camera video surveillance system lacks a coordination mechanism between abnormal event response and dynamic scheduling of computing resources, resulting in the failure to optimize resource utilization.

Method used

By combining the GPU/NPU edge intelligent computing platform, combining the object detection algorithm, dynamically adjusting the sampling interval and computing resource allocation of the camera, giving priority to allocating more resources to the cameras that detect abnormal events, reducing the computing load of the camera without abnormal events.

Benefits of technology

It realizes efficient detection and response to abnormal events under limited computing resources, improves the overall efficiency and resource utilization of the system, ensures that important cameras obtain more computing resources when abnormalities, and reduces the computing burden of ordinary cameras.

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Abstract

The invention discloses a reasoning resource allocation optimization method and system for multi-camera real-time video monitoring, and relates to the field of multi-camera real-time monitoring. According to the invention, through reasonable reasoning of resource allocation and combination of a video frame rate dynamic adjustment strategy, optimization processing of video streams of a plurality of cameras is realized. Particularly, in the method, different reasoning resources are allocated according to the priorities of the detection tasks of different cameras, so that the cameras with abnormal event detection requirements obtain more computing resources, and the cameras without abnormal events use fewer resources, thereby achieving the balance between the optimal performance and the computing resources.
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