Gaussian mixture model-based moving target detection method

Abstract

The invention discloses a Gaussian mixture-based moving target detection method. The method comprises the following steps of 1) building a Gaussian mixture model; 2) obtaining a changed region through difference, and performing measurement and analysis judgment on an area of the changed region; 3) analyzing and judging whether light sudden change occurs in the changed region or not; 4) adopting different moving target detection methods according to a judgment result of the step 3); and 5) detecting out a moving target. According to the method, the moving target is detected by selectively adopting a background difference method or an inter-frame difference method through measuring and judging the area of the changed region and judging whether the light sudden change occurs in the changed region or not by adopting a background reconstruction mechanism updated in a cyclic period; and the method well solves the problem that a conventional background subtraction method is relatively sensitive to the illumination change, also solves the problem that a relatively long-time virtual shadow is easily generated during conversion between a foreground and a background in a scene, and has relatively good robustness.

Description

technical field [0001] The invention belongs to the technical field of video monitoring and relates to a moving target detection method based on a mixed Gaussian model. Background technique [0002] Intelligent video surveillance refers to the use of computer vision analysis methods to automatically analyze video sequences without human intervention, to achieve moving target detection, classification, identification, tracking, etc., and on this basis, through pre-set rules Analyze the target's behavior to provide a reference for further action. Among them, the purpose of motion detection is to determine whether there is a moving target in the monitoring scene through the analysis of the surveillance video image sequence, and then extract the motion area from the detection image. Accurate and effective segmentation of moving regions is the basic premise for subsequent processing such as moving target tracking, classification and recognition. [0003] Currently, moving targe...

AI Technical Summary

Problems solved by technology

However, when the mixed Gaussian model changes from the background to the foreground moving object for a long time stagnant object, the update speed of the background model is slow; when the object changes from static to moving and escapes from the background, it may mistakenly detect the background exposure area as the foreground. In the detection result "Shadows" of foreground objects appear

In addition, the background difference method based on the mixed Gaussian model is based on a fixed and static background. However, the actual environment is always complex and changeable, such as illumination changes, background disturbances, and slight shakes of the camera, etc., will affect the detection of moving objects. Sensitivity and Accuracy

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