MATLAB Simulation Realization of Video Background Extraction Algorithm Based on Gaussian Mixture Model
Video background extraction is one of the widely used techniques in computer vision, which can separate the foreground and background in a video. In video surveillance, face recognition, image recognition and other fields, background extraction technology has very important applications. This article will introduce the MATLAB simulation implementation of the video background extraction algorithm based on the Gaussian mixture model.
Gaussian mixture model is a common density estimation method, which is also widely used in background extraction. The main idea is to regard each pixel in the image as composed of two parts, background and foreground, and use multiple Gaussian distributions to model these two parts. By comparing the pixel value with the probability value of each Gaussian distribution, it is judged as foreground or background. In practical applications, we use the weighted sum of multiple Gaussian distributions to represent the distribution of the entire pixel, that is, the Gaussian mixture model.
Below, we will introduce the MATLAB simulation implementation of the Gaussian mixture model background extraction algorithm.
First, we need to read in a video file and initialize some parameters. Among them, the parameters of the GMM model include: the mixture component score K, the weight, mean and standard deviation of each mixture component, and the threshold for classifying pixels as background, etc.
% 读入视频文件
video = VideoReader('test.mp4');
% 初始化参数
K = 3; % 混合成分数
alpha = 0.01; % 学习速率
T = 0.9; % 背景阈值
w = zeros(video.Height, video.Width, K); % 权值
u = zeros(video.Height, video.Width, K); % 均值
sigma = zeros(video.Height, video.Width, K); % 标准差
p = ones(video.Height, video.Width) * 1/K; % 每个像素属于背景的概率
Next, we need to classify each pixel and update the mean and variance. The specific implementation process is as follows: