Automatic generating method for multi-lane vehicle track space-time diagram

Abstract

The invention relates to an automatic generating method for a multi-lane vehicle track space-time diagram. The method comprises the steps that firstly, a camera is utilized for obtaining internal parameters and external parameters of the camera through off-line calibration, an area of interest is set according to the actual monitoring requirement, and three virtual detecting belts are set at the upstream initial position of the traffic flow; secondly, the vehicle feature points are extracted based on the three virtual detecting belts, the feature point tracking and grouping correction are carried out according to vehicle grouping, the vehicle tracks are extracted in a high-precision mode, a longitudinal space-time track diagram of passing vehicles is automatically generated for each lane, and a transverse space-time track diagram comprising all lanes is generated; finally, whether it is in the lane line correction stage or not is automatically judged, wherein if yes, the lane line position is automatically corrected based on the track clustering method. Compared with the prior art, the method improves the robustness of the target luminance change, improves the precision of vehicle detecting and track tracing, is capable of well automatically correcting the lane line on line and avoids misjudgment on the lane number to which the vehicle track point belongs.

Description

technical field [0001] The invention belongs to the technical field of traffic and transportation monitoring, which uses computer video processing technology and pattern recognition technology to automatically extract road moving vehicle trajectories, and on this basis, automatically distinguishes the lane to which the vehicle belongs, and generates a longitudinal space-time map of vehicle trajectories in each lane and a horizontal space-time map of road sections. Background technique [0002] The space-time diagram of vehicle trajectory is the "time-position" coordinate representation during vehicle driving, in which time is usually taken as the abscissa axis, and the spatial position (longitudinal or horizontal) of the vehicle in the lane is the ordinate axis. The space-time map of vehicle trajectories has important application value in the field of transportation technology. It can not only provide a unified support platform for the acquisition of various traffic paramete...

AI Technical Summary

Problems solved by technology

The method based on motion foreground extraction and tracking can obtain the vehicle trajectory well when the traffic flow is smooth, but when there is traffic jam, slow traffic flow and mutual occlusion, the effect of this method will drop sharply or even fail

The method based on feature point extraction and tracking can better solve this problem, but it has a large amount of calculation, and the algorithm has poor real-time performance without special hardware support. At the same time, it comes from the interference of highly similar motion characteristics of adjacent vehicles and The interference of vehicle activity shadows also greatly affects the accurate judgment of the vehicle to which the feature point belongs and reduces the accuracy of vehicle tracking.

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