Image tracking method based on image frequency domain conversion

Abstract

The invention provides an image tracking method based on image frequency domain conversion. The method comprises the following steps: firstly, preprocessing input SAR image data; on the basis of a traditional delicate Lee filter, introducing a window size adaptive selection method based on equivalent opinions, and forming a filtering algorithm adaptive to the window size; secondly, establishing anSAR image dense sample application feature extraction algorithm based on a two-dimensional sinc function model; developing a multi-layer multi-window matching strategy, and determining the reliable matching window size in a self-adaptive mode according to the error measurement output by matching of different layers; developing a sub-pixel phase correlation algorithm based on multi-scale phase consistency characteristics, constructing multi-scale phase consistency structure images, and estimating the phase difference between the images in a frequency domain space. Through the verification of the specific embodiment, the tracking method provided by the invention can effectively track the target feature image, and improves the accuracy and robustness.

Description

technical field [0001] The invention relates to an image tracking method, in particular to an image tracking method based on image frequency domain conversion. Background technique [0002] As an important application in the field of computer vision and image processing, image tracking is currently being used in video surveillance and human-computer interaction. Since people discovered the great experimental value of target tracking and the strong demand for computer technology in the last century, developed countries such as the United States and Europe have begun to conduct in-depth research on target tracking technology. After years of research, the mainstream algorithms can be roughly divided into the following categories: (1) Detection-based methods: start directly from the entire image to find the difference between the target and the background, and use this difference to directly detect the difference between the two images. Distinguish objects from background. (2)...

AI Technical Summary

Problems solved by technology

[0004] However, in the prior art, most of the unavoidable noise problems in the image are filtered by various filtering windows.

People want to suppress noise as much as possible and retain most of the details. The size of the filter window has an important impact on denoising and the preservation of details, but it is difficult to determine an appropriate window size.

Secondly, a large number of weak texture areas in the image may cause image matching mismatches, so how to reduce the required matching feature points through feature extraction and try to ensure that the matching window does not only contain weak texture areas is also a key issue.

Finally, the accuracy of image matching is a key factor in determining the effect of image-based target tracking, and the accuracy and success rate of image matching are easily affected by sensor noise and scene noise

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