Multi-target collaborative tracking method for large scenes, intelligent monitoring system, traffic system

Abstract

The invention belongs to the technical field of camera control devices, and discloses a large-scene multi-target collaborative tracking method, an intelligent monitoring system, and a traffic system. The target can still be accurately segmented when the target is interlaced by master-slave collaboration, and the tracking robustness is improved. The master is a fixed wide-angle camera, namely the trigger, and the slave is a high-resolution zoom dome camera, namely the dome camera. The present invention includes a gun camera and ball camera relationship module, a target detection module, a priority judgment module, a control module, a target update module and a tracking module. The invention classifies the multi-target relationships in the bolt picture with priority, and determines the action of the dome machine according to the target relationship, which improves the utilization rate of the dome machine and saves resources; High-definition images are used for target detection, which improves the accuracy of target detection. Through the relationship matrix between the gun and the ball, the detection information of the ball machine screen is mapped back to the gun machine screen, and then the interlaced area of ​​the target is re-segmented to achieve collaborative tracking.

Description

technical field [0001] The invention belongs to the technical field of camera control devices, and in particular relates to a large-scene multi-target cooperative tracking method, an intelligent monitoring system, and a traffic system. Background technique [0002] The detection and tracking of moving objects has always been a research hotspot in the field of image processing and computer vision. With the continuous deepening of the research on detection and tracking methods, people have higher and higher requirements for the performance of target detection and tracking, and the application scenarios of target detection and tracking are becoming more and more complex. How to improve the robustness of multi-target detection and tracking under special conditions has always been an issue It is one of the hot issues studied by scholars. Among them, the collaborative tracking of multiple targets in large-scale scenarios has always been a very important research direction in the ...

AI Technical Summary

Problems solved by technology

This method cannot separate the target independently in the target staggered area, and only uses the trajectory prediction method to predict and track the position of the target, which is difficult to achieve robust tracking

[0003] In summary, the problems in the prior art are: The existing small target anti-occlusion tracking methods in large scenes only use the trajectory prediction method to predict and track the position of the target when the targets are staggered, but when the targets are staggered, the avoidance between targets is easy to appear The change of motion speed and direction after staggering, while the trajectory prediction method uses the motion state data of the target before staggering, it is very easy to have a difference between the predicted position and the actual position of the target, especially when three or more targets are staggered. There is a large difference in position. After the target interleaving is over, tracking errors or tracking loss are prone to occur

[0004] The difficulty and significance of solving the above technical problems: In a large scene, the target is small, and the feature difference between the targets is not obvious. When multiple targets are interlaced, it is difficult to segment a single target. At the same time, the motion state of the target is prone to large changes. It is difficult to accurately predict only relying on the motion information of the target before interlacing. The position after the target stagger ends, which is easy to cause tracking errors or loss

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