Static target detection method based on two-background differencing

Abstract

The invention discloses a static target detection method based on two-background differencing. The method comprises the steps as follows: acquiring a video image; constructing a background model; detecting a moving target; establishing a pure background video image; detecting a foreground target; detecting a static target. Background modeling is performed by use of an improved Gaussian mixture model, and the method can be applied to accurate modeling of backgrounds having slow illumination change or repeated moving objects in complex scenes, particularly to conditions with slow illumination and weather change or higher moving object speed; by reducing the number of Gaussian distribution functions established for each pixel, the calculation amount is reduced, and the real-time performance is improved. Foreground object extraction is performed with a background differencing method, the principle and algorithm design are simple, the obtained result directly reflects the position, size and shape of a foreground object, and more accurate foreground object information can be obtained. A two-background model algorithm is adopted for static object detection, so that the method is lower in complexity and easy to implement.

Description

technical field [0001] The invention relates to an application in a real-time intelligent video monitoring system, in particular to a method for detecting a stationary target in a real-time intelligent video monitoring system. Background technique [0002] The static target is an important monitoring target in the real-time video surveillance system, which has an important impact on the protection of human life and property and the maintenance of social public order. In the real-time intelligent video surveillance system, a static target refers to an object that was not in the original scene but then entered the scene and stayed for more than a certain period of time. [0003] At present, the target detection method based on background difference is widely used in real-time monitoring. The background difference method generally establishes the background model first, and then uses the background model and video sequence difference to obtain the foreground target. Using mix...

AI Technical Summary

Problems solved by technology

Using mixed Gaussian background modeling can better establish the background model and extract the foreground target, but if the stationary target stays for more than a certain period of time, it will be updated to the background as the background model is updated, and cannot be stably detected

Moreover, the traditional mixed Gaussian background modeling has a large amount of calculation and takes a long time, which is no

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