Moving target detection method with combination of region extraction and improved texture feature

Abstract

The invention discloses a moving target detection method with the combination of region extraction and an improved texture feature. The method comprises the steps of (1) collecting successive images in a monitoring video as a sampling frame, (2) carrying out background modeling and recovery on each pixel in the sampling frame by using its sampling information, (3) segmenting the image, extractinga foreground region by using statistical features of image blocks, judging the illumination change in the foreground region, and determining whether the second judgment of the foreground region is needed, and (4) carrying out the accurate extraction of foreground pixel points in the foreground region. By performing fast foreground region extraction, the amount of calculation for subsequent accurate judgment is greatly reduced, while the region extraction is carried out, two main interferences including a spatial displacement interference (leaf sway, etc.) and a brightness variation interference (lighting change, etc.) are eliminated, and a moving target in the image sequence is accurately and efficiently extracted.

Description

technical field [0001] The invention relates to a moving target detection method combining region extraction and improved texture features, and belongs to the technical field of visual detection. Background technique [0002] Image sequence-based moving object detection technology is the basis of many high-level computer vision processing behaviors, such as object tracking, behavior understanding, abnormal behavior analysis, etc. The integrity and validity of moving object detection results are critical to follow-up research. At present, most moving object detection algorithms directly judge the image frames to be processed point by point, so as to realize the accurate detection of moving objects. This point-by-point judgment method is easily affected by noise points (lighting, dynamic background, and imaging equipment errors, etc.), misjudging a large number of dynamic noise points as foreground, and in some moving scenes, the moving target occupies The area is small, and ...

AI Technical Summary

Problems solved by technology

This point-by-point judgment method is very susceptible to the influence of noise points (illumination, dynamic background, and imaging equipment errors, etc.), and a large number of dynamic noise points are misjudged as foreground, and in some moving scenes, the proportion of moving objects The area is small, and point-by-point judgment will waste a lot of computing resources in some background areas that do not have obvious foreground features

Moreover, when performing noise interference elimination, most existing algorithms treat all interference points (illumination, swaying leaves, and noise points) as sparse noise for elimination. Methods that consider noise characteristics are limited for different types of noise cancellation

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