A self-adaptive traffic violation identification method for highway scenes

By building the HRnet V2 road segmentation model in monitoring equipment and combining the differences in image background and road elements to judge changes in camera angle, the problem of misjudgment when adjusting the camera angle of monitoring equipment is solved, and fast and accurate identification of traffic violations on highways is achieved.

CN117058634BActive Publication Date: 2025-10-03TRAFFIC MANAGEMENT RES INST OF THE MIN OF PUBLIC SECURITY
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Patent Information

Application Number
CN202311109919.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-08-31
Publication Date
2025-10-03
Estimated Expiration
2043-08-31

AI Technical Summary

Technical Problem

Existing monitoring equipment is unable to adaptively adjust the monitoring scene when adjusting the camera angle, resulting in frequent misidentification of traffic violations on highways.

Method used

HRnet V2 is used to build a road segmentation model. The camera angle changes are judged by the differences between image background and road elements. The basic road image is updated in combination with a multi-frame fusion algorithm to quickly and accurately identify traffic violations.

Benefits of technology

It effectively reduces the probability of misjudgment in the identification of traffic violations on highways and improves the computing efficiency and accuracy of monitoring equipment in large data scenarios.

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Abstract

The present invention provides a method for adaptively identifying traffic violations in highway scenarios. This method can quickly and accurately detect changes in camera monitoring angles, effectively reducing the probability of misidentification of highway violations. While monitoring equipment is monitoring vehicle violations, it first determines the monitoring angle of the monitoring equipment based on background changes in the video image data. It then uses the video image data that indicates changes in the monitoring angle to extract road elements using a road segmentation model, and accurately determines whether the monitoring angle has changed based on these road elements.
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