Background modeling method based on brightness and texture fusion threshold value

Abstract

The invention discloses a background modeling method based on a brightness and texture infusion threshold value. The method comprises: a brightness and texture fusion threshold value is calculated; with a VIBE algorithm, all pixel points at one frame of image are classified into two types: foreground pixels and background pixels; Gaussian mixture modeling is carried out on pixel points with changing brightness in a new image frame; and then a threshold value corresponding to each pixel point is updated. According to the method, because the texture and color brightness are fused to form a threshold value and advantages of the Gaussian mixture model and the VIBE algorithm are combined, the background can be extracted accurately under the circumstance that several kinds of external disturbances like an illumination change, slight camera shaking, a dynamic background element exist, the influence on the real moving target by shadows can be suppressed to a certain extent, the anti-interference capability is enhanced, the image frame processing is accelerated, and the moving target segmentation precision is improved effectively.

Description

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AI Technical Summary

Benefits of technology

This patented technology describes an improved way to process images by combining both features from different techniques like histogram analysis or grayscale projection. By calculating this combination' thresholds, we are able to accurately detect any variations that may affect the quality of displayed content without being affected by other factors like ambient conditions. Overall, it provides technical benefits over existing methods.

Problems solved by technology

This patented technical solution describes how we want to accurately detect small movements or areas within images captured on cameras due to varying environmental conditions like sunlight exposure, movement caused by other things around them, and even if they move slightly during capturing. These issues make accurate automatic focusing techniques crucial but may be hard to achieve without prior knowledge about their nature.

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