Background reconstruction method based on gray extremum

Abstract

The invention discloses a background reconstruction method based on gray extremum. The method specifically comprises the following steps: N-frame image sequences acquired by image acquisition equipment are read in a computer system to serve as background images for reconstructed scenes; the pixel gray scale based on gray extremum is classified; weights of all gray region types are respectively calculated by the following formula; and the pixel background is selected. By adopting the method, the gray region types are divided through the gray extremum, and background gray scale values of pixelsare selected to build the scene background, so that the storage space is saved, and the calculated amount is small; the background and the target in the scene are not required to be modeled, so that mixture is effectively avoided; background reconstruction can be accurately carried out, and the robustness is good; and the method has broad application prospect in the real-time system fields, such as machine vision, video surveillance, military science, urban traffic monitoring, inhabitant routine safety monitoring and the like.

Description

technical field [0001] The invention relates to a background reconstruction method applied to moving target detection and tracking, in particular to a background reconstruction method based on gray extreme value. Background technique [0002] Video sequence moving target detection has broad application prospects in intelligent monitoring systems, machine vision, military science and other fields. It can automatically extract and locate moving targets in video sequences without human intervention, and analyze them It can provide the basis for subsequent target identification, tracking, alarm and recording, and can also respond in time when abnormal situations occur. [0003] At present, the commonly used video sequence moving target detection methods mainly include optical flow method, frame difference method and background difference method. The optical flow method can be applied to the situation of camera movement, but its calculation amount is very large, and it is sensit...

AI Technical Summary

Problems solved by technology

The model method is to establish a statistical model for the gray level of each pixel, such as a Gaussian distribution model, a mixed Gaussian distribution model, etc., and use an adaptive method to update the background by adjusting the model parameters. This method is only available when the scene does not contain moving objects. Only when the model can be initialized correctly, there are certain difficulties in practical application; the gray level classification method is based on certain assumptions, using a continuous video sequence that has been observed to select the pixel gray level to obtain the current background image, such as: based on The background reconstruction technology of pixel classification, this method has a small amount of calculation, but when the pixel gray level changes slowly, it will produce wrong reconstruction results

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