Method, system and device for realizing scene change video analysis and detection based on high-speed dome camera, processor and storage medium

Abstract

The invention relates to a method for realizing scene change video analysis and detection based on a high-speed dome camera. The method comprises the following steps: performing multi-angle high-precision detection and identification on a basic target, and performing unsupervised clustering learning according to the detected target and a driving track to complete space reconstruction of a traffic monitoring scene; analyzing the driving track model, determining a lane direction, and completing reconstruction of a traffic incident detection rule; detecting targets appearing in the traffic scene, establishing traffic flow data containing multiple target movement tracks, and judging traffic events through analysis of the traffic flow data. The invention also relates to a corresponding system and device, a processor and a computer readable storage medium. By adopting the method, the system, the device, the processor and the computer readable storage medium, difficult demands and technical problems existing in current traffic management are effectively solved, the current situation of traffic management is improved, technical bottlenecks confronted in the industry are effectively broken through, and updating and rapid development of the intelligent traffic industry are greatly promoted.

Description

technical field [0001] The present invention relates to the field of transportation industry, in particular to the field of intelligent analysis applications, and specifically refers to a method, system, device, processor and computer-readable storage medium for realizing scene change video analysis and detection based on a high-speed dome camera. Background technique [0002] At present, the intelligent analysis application of traffic incidents has been widely used in the transportation industry, and intelligent transportation product manufacturers have also launched various products. However, as more and more traffic cameras are deployed in the scene, the human work of scene modeling, the adaptive ability of the model and even the life cycle of the project product will be significantly affected. [0003] (1) First of all, in the current scene algorithms provided by intelligent transportation manufacturers, the modeling and configuration are basically done manually. Accordi...

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Problems solved by technology

Therefore, the more cameras, the greater the configuration work, which is time-consuming and labor-intensive;

[0004] (2) Secondly, due to manual configuration, once the modeling is completed, the configuration information must match the real scene

In fact, this approach is equivalent to turning a fastball into multiple fixed bolts, but it adds more workload

In addition, there are still many disadvantages that cannot be quickly modeled for temporary custom perspectives.

[0006] (4) Artificial modeling still has limitations. Although the influence of weather, light, and human control cannot achieve adaptive self-learning ability, it cannot cope with large-scale deployment

Once the accuracy drops, it will lead to more false positives, so that intelligent analysis products become useless

Therefore, it must be equipped with experienced engineers for a long time to continuously correct and debug

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