matlab光流法前景分割,背景建模运动目标跟踪 matlab程序

题目：背景建模和前景分割的方式把运动车辆提取出来。并进行最近临关联，输出目标轨迹。

混合高斯模型使用K(基本为3到5个) 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。

程序：(参考其他程序改进而得)

% mixture of Gaussians algorithm for background混合高斯模型背景建模

%混合高斯模型适用于相机固定的运动目标检测，光流法适用于相机运动的运动目标检测

close all;

clear all;

source=dir('*.bmp');

% -----------------------  frame size variables -----------------------

% read in 1st frame as background frame

fr=imread(source(1).name);

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)   Cgegaosimoxing

M = 3;                                  % number of background components,

D = 2.5;                                % positive deviation threshold

alpha = 0.01;                           % learning rate (between 0 and 1) (from paper 0.01) the background will change slowly with the time, so the mean(u)should be updated slowly.

thresh = 0.25;                          % foreground threshold (0.25 or 0.75 in paper)

sd_init = 6;                            % 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  , the u of gaosi(u,d)

sd = zeros(height,width,C);             % pixel standard deviations , the d of gaosi(u,d)

u_diff = zeros(height,width,C);         % difference of each pixel from mean

p = alpha/(1/C);                        % initial p variable (used to update mean and sd)

rank = zeros(1,C);                      % rank of components (w/sd)

% --------------------- initialize component means and weights -----------

pixel_depth = 8;                        % 8-bit resolution

pixel_range = 2^pixel_depth -1;         % pixel range (# of possible values)

for i=1:height

for j=1:width

for k=1:C

mean(i,j,k) = rand*pixel_range;     % means random (0-255)

w(i,j,k) = 1/C;                     % weights uniformly dist

sd(i,j,k) = sd_init;                % initialize to sd_init

end

end

end

%--------------------- process frames -----------------------------------

for n = 8:(length(source)-2)              %there will be false route line,so it only include the pictures which has the car

fr = imread(source(n).name);       % read in frame

fr_bw = rgb2gray(fr);       % convert frame to grayscale

% calculate difference of pixel values from mean

for m=1:C

u_diff(:,:,m) = abs(double(fr_bw) - double(mean(:,:,m)));

end

sum_x=0;

sum_y=0;

num=0;

% update gaussian components for each pixel

for i=1:height                        %%%%%%%%%%%%%search each pixal of one image, if it is in the C ge gaosimoxing, it is belong to the background,and undate the background.

for j=1:width                     %%%%%%%%%% If it is not in the C ge gaosimoxing, create a new gaosi and replace the least possible gaosi. Finally the the first several gaosi is background, and the last several is foreground

match = 0;

for k=1:C

if (abs(u_diff(i,j,k)) <= D*sd(i,j,k))       % pixel matches component

match = 1;                          % variable to signal component match

% update weights, mean, sd, p

w(i,j,k) = (1-alpha)*w(i,j,k) + alpha;

p = alpha/w(i,j,k);

mean(i,j,k) = (1-p)*mean(i,j,k) + p*double(fr_bw(i,j));

sd(i,j,k) =   sqrt((1-p)*(sd(i,j,k)^2) + p*((double(fr_bw(i,j)) - mean(i,j,k)))^2);

else                                    % pixel doesn't match component

w(i,j,k) = (1-alpha)*w(i,j,k);      % weight slighly decreases

end

end

bg_bw(i,j)=0;

for k=1:C

bg_bw(i,j) = bg_bw(i,j)+ mean(i,j,k)*w(i,j,k);

end

% if no components match, create new component

if (match == 0)

[min_w, min_w_index] = min(w(i,j,:));

mean(i,j,min_w_index) = double(fr_bw(i,j));

sd(i,j,min_w_index) = sd_init;

end

rank = w(i,j,:)./sd(i,j,:);             % calculate component rank

rank_ind = [1:1:C];

% calculate foreground

fg(i,j) = 0;

while ((match == 0)&&(k<=M))

if (abs(u_diff(i,j,rank_ind(k))) <= D*sd(i,j,rank_ind(k)))

fg(i,j) = 0; %black = 0

else

fg(i,j) = fr_bw(i,j);

sum_x=sum_x+j;

sum_y=sum_y+i;

num=num+1;

end

k = k+1;

end

end

end

if n==8||n==9||n==10

route_x=[round(sum_x/num),round(sum_x/num)];

route_y=[round(sum_y/num),round(sum_y/num)];

else

next_x=round(sum_x/num);

next_y=round(sum_y/num);

route_x=[route_x,next_x];

route_y=[route_y,next_y];

end

num=0;

figure(1);

subplot(3,1,1);

imshow(fr);

title('原始图像');

subplot(3,1,2);

imshow(uint8(bg_bw));

title('背景图像');

subplot(3,1,3);

imshow(uint8(fg));

hold on;

plot(route_x,route_y,'LineWidth',1,'Color','r');

title('前景图像');

%Mov1(n)  = im2frame(uint8(fg),gray);           % put frames into movie

%Mov2(n)  = im2frame(uint8(bg_bw),gray);           % put frames into movie

end

%movie2avi(Mov1,'mixture_of_gaussians_output','fps',30);           % save movie as avi

%movie2avi(Mov2,'mixture_of_gaussians_background','fps',30);           % save movie as avi

运行结果：