Background modeling and motion object detection method and apparatus with image gradient and gray scale integration

Abstract

The invention provides a background modeling and motion object detection method with image gradient and gray scale integration. The method comprises: 1, collecting N frames of video monitoring data as a training sample; 2, establishing an image-gray-scale-based single Gaussian model; 3, selecting pixel points to establish a gradient model; and 4, carrying out moving object detection. In addition, the invention also provides a background modeling and motion object detection apparatus with image gradient and gray scale integration. The apparatus consists of an image acquisition unit, a gray scale background modeling unit, a gradient background modeling unit, a model judging unit, a moving object detection unit and a background model updating unit. According to the invention, backgrounds are distinguished into a single back ground and a complicated background based on a gray scale standard difference and different background models are established for description; and with the single background model, the background can be described rapidly and effectively, and with the complicated background model, the complicated background change can be described well. Therefore, accuracy of the moving object detection can be improved well.

Description

technical field [0001] The invention relates to the technical field of image monitoring. Background technique [0002] Background modeling technology is the key technology of moving object detection, and it is also the most commonly used method for moving object detection. It is very helpful for image analysis, video content analysis effect, feature extraction, etc. However, in actual application scenarios, the background is not completely fixed, and there are problems such as background motion, illumination changes, camera shake, etc., resulting in a simple background model that cannot describe the background well, and more false detections, Missing detection and other situations will affect the effect of further analysis on images and videos. [0003] At present, the ideal background modeling methods include: mixed Gaussian model, codebook method, ViBe and PBAS and other methods. The mixed Gaussian model uses one or more Gaussian distributions to represent the background...

AI Technical Summary

Problems solved by technology

The ViBe algorithm handles the sudden change of the background better, and simplifies the modeling process better, but because the gray value of the pixel of the moving object is used as the sample value of the model, it is easy to cause false detection and smear area

Most of the current background modeling methods use pixel-based grayscale statistical methods, which can obtain more detailed moving objects, but use the same method to describe static pixels and moving pixels. When there are moving objects in the background, it cannot accurately describe the background objects. sports

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