Method and system for detecting and tracking aircraft landing marks

Abstract

The invention relates to the field of image processing, and discloses a method and system for detecting and tracking aircraft landing marks, so as to realize accurate and fast detection and tracking of the aircraft landing marks, and assist in completing the accurate landing of the aircraft; the method of the invention comprises: acquiring the aircraft landing marks Based on the information of two or more image frames collected by the video detection device on the upper side, determine the target area corresponding to the landing mark in the initial image frame, then determine the minimum circumscribed rectangle of the target area, and use the library function to determine the coordinates of the four corners of the minimum circumscribed rectangle; The next frame of the image frame is used as the current image frame, and the affine transformation matrix between the initial image frame and the current image frame is calculated, and the position of the coordinates of the four corner points in the initial image frame in the current image frame is predicted according to the affine transformation matrix, and The predicted rectangular frame is marked in the current image frame; the current image frame is taken as the new initial image frame, and the current frame is re-determined, and the above steps are repeated to complete the tracking of the landing mark.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method and system for detecting and tracking aircraft landing marks. Background technique [0002] With the rapid development of the computer industry, the target detection and tracking technology based on computer vision has also been greatly improved. At present, the target detection methods mainly include edge-based corner feature detection methods, circle feature-based detection methods, The detection method based on invariant moments, and the target detection method based on deep learning; the target tracking method mainly includes the method based on template matching, the tracking method based on the region, the dynamic contour tracking algorithm, and the particle filter tracking algorithm; among them, the edge-based Although the corner point detection method has high precision and good stability, the corner point has a great dependence on the edge extraction algorithm, a...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of the computer industry, the target detection and tracking technology based on computer vision has also been greatly improved. At present, the target detection methods mainly include edge-based corner feature detection methods, circle feature-based detection methods, The detection method based on invariant moments, and the target detection method based on deep learning; the target tracking method mainly includes the method based on template matching, the tracking method based on the region, the dynamic contour tracking algorithm, and the particle filter tracking algorithm; among them, the edge-based Although the corner point detection method has high precision and good stability, the corner point has a great dependence on the edge extraction algorithm, and the broken edge line has a great influence on the corner point detection; It depends on the accuracy of the graphic contour data, and the amount of calculation is large; the target detection method based on the invariant moment can detect the target well, but the computational complexity of the invariant moment is high, and it is only suitable for processing simple two-dimensional image

The tracking method based on model matching can be applied in the complex environment and the situation where the target pose changes, but this method needs to calculate a complex model, and the calculation amount is very large; the area matching algorithm has a large amount of calculation, and in the case of occlusion and light intensity changes, This method may fail to track; the disadvantage of the dynamic contour tracking method is that it is sensitive to the initial value of the target. Once the initial target contour parameters are detected incorrectly, the subsequent tracking process will be completely invalid.

The particle filter algorithm itself is relatively complex, with a large amount of calculation and a large storage space; the target detection method based on deep learning has a very high accuracy rate, but the increasingly high-dimensional feature description brings a burden to the calculation, and the classifier model The pre-training of requires a huge amount of data, and one model only corresponds to one target situation

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