Open AccessAn improved Gaussian Mixture Model algorithm for background representation
Author(s) -
Ping Wang,
Shaoxiong Dong
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012148
Subject(s) - initialization , mixture model , frame (networking) , variance (accounting) , gaussian , computer science , algorithm , representation (politics) , frame rate , object (grammar) , artificial intelligence , pattern recognition (psychology) , telecommunications , physics , accounting , quantum mechanics , politics , political science , law , business , programming language
Initializing a background frame for Gaussian Mixture Model requires no moving objects in the background scene. In this paper, in order to obtain an initial frame when there is a moving object in the background scene, filtering algorithm is used for background frame initialization. This paper proposes an improved method for updating Gaussian mixture models. In the initial stage of the GMM, the update rate of the mean and variance is taken as a larger value, so that the model mean and variance update speed becomes faster, and the model learning speed is accelerated; after training for a period of time with a large update rate, let The mean update rate is unchanged, and the variance update rate becomes smaller, so that the background model can be more stable.