Field moving object fining extraction method

Abstract

The invention relates to a field moving object fining extraction method, which comprises the following steps that: a sequence image including a moving object is obtained, and the sequence image is preprocessed; the preprocessed sequence image is subjected to frame-by-frame differentiation, and is then segmented into a plurality of grids, a located moving region of the object is determined according to feature values of the grids, and a grid method is used for extracting a moving region of the object for further reducing an object range; the background is subjected to modeling in the moving region of the object, a binaryzation chart of the object is obtained through a background subtraction method, and the binaryzation chart is subjected to feedback pixel processing; the processed binaryzation chart is mapped to a located color chart region of the object, and the color chart region is subjected to super pixel segmentation; and the segmentation result is fused with the binaryzation chart, the confidence degree of each super pixel is calculated according to the fusion result, and after the thresholding, the fining moving object is finally obtained. The field moving object fining extraction method can realize the real-time robust and fine field moving object extraction under complicated backgrounds.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for finely extracting a moving target in the field. Background technique [0002] Automatically extracting moving targets from real scenes is of great significance in security monitoring, precise guidance and tracking, unmanned ground guard systems, wireless sensor networks, and many other aspects. Especially in military applications, the background is often very complex, such as jungle backgrounds in the field, mountain backgrounds, and sea and air backgrounds under severe weather conditions. In such a complex environment, it is difficult to obtain better results with traditional target extraction algorithms, mainly because the process of moving targets is not a nonlinear and non-Gaussian dynamic process. [0003] The extraction of moving targets is an important prerequisite for target recognition, and the quality of its extraction is directly related to the a...

AI Technical Summary

Problems solved by technology

It can be summarized as follows: 1. Obtained in a more ideal background; 2. The real-time requirements of the system are rarely considered; 3. The extracted moving target is usually a rectangular frame area, not the outline of the target itself

4. Most of the output is a rough binary target image, which loses information such as color texture that is beneficial to target recognition

When these algorithms are used in complex backgrounds in the field, the extraction performance drops sharply, and the computational complexity is high, which cannot meet the real-time requirements.

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