Object detection method based on adaptive-parameter-adjustment Gaussian mixture model

Abstract

The present invention provides an object detection method based on an adaptive-parameter-adjustment Gaussian mixture model. The object detection method comprises the steps of: acquiring edge data of an image sequence; establishing an adaptive-parameter-adjustment Gaussian mixture model for an edge detection sequence, wherein modeling is performed for each pixel point of the edge data through adoption of a combination of k Gaussian distributions, for the successfully matched Gaussian distribution, update rate of the weight decreases along with the increase of the number of frames, and update rate of the mean and variance decreases along with the increase of the historical number of matching of Gaussian distributions, for the unsuccessfully matched Gaussian distribution, parameters are kept unchanged; finally detecting the edge of the image sequence by using the established Gaussian mixture model to obtain a foreground object profile, and filling the foreground object profile to obtain an integral foreground moving object. Compared with a traditional object detection method based on a Gaussian mixture model, the object detection method of the present invention has the beneficial effects that the foreground moving object can be detected more accurately when lights change, and computing speed is raised by 28.574%.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a Gaussian mixture model target detection method with parameter adaptive adjustment. Background technique [0002] Target detection is the basis for subsequent target tracking work. A good target detection algorithm can extract the area of ​​interest in the image frame or the detection of the moving target to be studied, and ensure that the extracted target is complete and does not contain Detection results for regions of no interest. In addition to the quality of the detection effect, the time complexity of the detection algorithm is also an important measure. The time complexity of the detection algorithm is closely related to the establishment mechanism of the background model, the update method of the background model, and the mechanism of identifying the foreground target. [0003] 1999年,Grimson和Stauffe(参考文件1:GrimsonWel,StaufferC.RomanoR.LeeL.Usingadaptivetrackingtoclassifya...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings of the above-mentioned existing Gaussian mixture model target detection method that are greatly affected by illumination changes, slow computing speed and slow model convergence speed.

Images