An AI-based router, gateway and camera video enhancement method and system

By integrating a lightweight multi-task AI model built into the camera and coordinating management with a policy control center, the system addresses the shortcomings in enhancement effects, real-time performance, and adaptive capabilities in video surveillance, achieving efficient and real-time video quality improvement suitable for large-scale security monitoring.

CN122138005APending Publication Date: 2026-06-02FUJIAN NEWLAND COMM SCI TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
FUJIAN NEWLAND COMM SCI TECH
Filing Date
2026-01-12
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies in video surveillance suffer from limited enhancement effects, poor real-time performance, high resource consumption, and insufficient adaptability, making it difficult to achieve efficient video quality improvement, especially in complex environments.

Method used

By embedding a lightweight multi-task AI model in the camera, combined with environmental perception and a policy control center, real-time video enhancement processing is achieved, enhancement strategies are dynamically selected, and collaborative management is carried out through routers or edge gateways to avoid bandwidth consumption and privacy risks.

Benefits of technology

It achieves efficient, real-time, and adaptive video quality improvement in complex scenarios, reduces bandwidth consumption and privacy risks, improves resource utilization efficiency and system robustness, and is suitable for large-scale security monitoring applications.

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Abstract

This invention provides an AI-based method and system for enhancing router, gateway, and camera video in the field of intelligent security and video surveillance technology. The method includes: Step S1, the camera performs real-time perception of the imaging environment of the acquired raw video stream to obtain environmental perception results; Step S2, based on the environmental perception results, it determines whether to start AI video enhancement processing and dynamically selects an enhancement processing strategy that matches the imaging environment; Step S3, a lightweight multi-task AI model performs real-time enhancement processing on the raw video stream according to the enhancement processing strategy to generate an enhanced video stream; Step S4, through a router or edge gateway connected to the camera, the enhancement processing strategy is uniformly distributed to the corresponding cameras in the local area network for collaborative monitoring and management. The advantages of this invention are: it greatly improves the real-time performance, enhancement effect, resource utilization efficiency, and scene adaptability of camera video enhancement, while effectively reducing bandwidth consumption and privacy risks.
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