使用混合高斯模型进行运动目标检测

>> % This m-file implements the mixture of Gaussians algorithm for background
% subtraction.  It may be used free of charge for any purpose (commercial
% or otherwise), as long as the author (Seth Benton) is acknowledged.

clear all
% source = aviread('D:\j');
source = aviread('D:\j.avi');
% -----------------------  frame size variables -----------------------
fr = source(1).cdata;           % read in 1st frame as background frame
fr_bw = rgb2gray(fr);     % convert background to greyscale
fr_size = size(fr);             
width = fr_size(2);
height = fr_size(1);
fg = zeros(height, width);
bg_bw = zeros(height, width);
% --------------------- mog variables -----------------------------------
C = 3;                                  % number of gaussian components (typically 3-5)
M = 3;                                  % number of background components
D = 2.5;                                % positive deviation threshold
alpha = 0.00001;                           % learning rate (between 0 and 1) (from paper 0.01)
thresh = 0.6;                          % foreground threshold (0.25 or 0.75 in paper)
sd_init = 15;                            % initial standard deviation (for new components) var = 36 in paper
w = zeros(height,width,C);              % initialize weights array
mean = zeros(height,width,C);           % pixel means
sd = zeros(height,width,C);             % pixel standard deviations
u_diff = zeros(height,width,C);         % difference of each pixel from mean
p = alpha/C;                        % initial p variable (used to update mean and sd)
pu = 0.01;
psd = 0.0005;
pw = 0.0005;