A Method for Extracting Stable Tracking Points of Extended Targets Based on Local and Global Coupling

Abstract

The invention discloses a method for extracting extended target stable tracking points based on local and global coupling. First, denoising, enhancement and other preprocessing are performed on the image to be processed; then the GMS algorithm is used to match the feature points of two adjacent frames of images, and then the generalized Hough transform is performed on these matching feature points to find the features that contribute to the peak point of the accumulator points as the basic feature points; use these basic feature points to obtain the homography mapping matrix of the feature points of two adjacent frames, and finally use the homography mapping matrix to calculate the feature of a sub-pixel precision mapped from the previous frame tracking point to the current frame point as a tracking point. The tracking point obtained by the present invention is a virtual global feature point mapped by multiple local matching feature points on the target. It does not require the target to have stable and obvious features all the time. The translation, scale, illumination, blur, It is highly robust to changes in attitude, self-occlusion, and even part of the target area leaving the field of view.

Description

technical field [0001] The invention belongs to the fields of image processing, computer vision and target tracking, and in particular relates to an extended target stable tracking point extraction method based on local and global coupling. Coupled extended target stable tracker extraction method. Background technique [0002] In the imaging tracking system, in order to improve the stability and accuracy of target tracking, a smaller field of view is generally used, resulting in the target occupying more than 1 / 3 of the field of view, and may even expand beyond the field of view. During the movement of the extended target, the pose changes obviously and some areas leave the field of view. At the same time, atmospheric turbulence and system jitter will cause translation and blurring of the target. In addition, local occlusion caused by attitude changes will also affect the stability of tracking. Selecting robust features to extract stable tracking points for extended target...

AI Technical Summary

Problems solved by technology

These methods can achieve a stable tracking effect when the target has obvious and unchanged features all the time, but when the target is partially occluded, part of the area leaves the field of view, or is severely rotated due to attitude changes, the selected features are no longer obvious or even When invisible, there will be jitter and drift or even tracking failure

At the same time, illumination changes, scale changes, blurring, etc. may also cause the original tracking feature points to fail.

In response to these situations, some researchers proposed to use the generalized Hough transform ([4]Ballard D H.Generalizing the Hough transform to detect arbitrary shapes[J].Pattern Recognition,1981,13(2):111-122.) to extract feature points Track extended target ([5] Hu J, Peng X. High precision localization for profile dextended object based on GHT [J]. Optik-International Journal for Light and Electron Optics, 2014, 125(3): 1297-1302.), in the target When some areas leave the field of view, this method can still lock the position of the original tracking point, but when the scale and attitude of the target change, and the outer contour changes greatly, the tracking point will drift

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