MATLAB Answers

Robust background substraction in outdoor environment

4 views (last 30 days)
sapphiregoh
sapphiregoh on 21 Mar 2016
Edited: Dima Lisin on 17 Apr 2016
Hi i need help for my final year project..i need background substraction coding for robust environment (including illumination changes) as i hardly find any reference coding in the internet for my ball detecting robot project. Need reply ASAP..thanks

Answers (3)

Joachim Schlosser
Joachim Schlosser on 21 Mar 2016
A final year project involves some aspect of research and discovery. MATLAB Documentation and MATLAB Central offer plenty of material if you search for it.

Image Analyst
Image Analyst on 21 Mar 2016
If the ball is moving, like you're following a tennis ball, then you can use optical flow. Or if it's a constant, known color you can use color segmentation. I have color segmentation demos in my File Exchange. http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862
  4 Comments
sapphiregoh
sapphiregoh on 16 Apr 2016
clear all
[fileFileName,PathName] = uigetfile('.wmv','Select video');
% ----------------------- frame size variables -----------------------
fr = videoReader(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 = 0.01; % learning rate (between 0 and 1) (from paper 0.01)
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
sd = zeros(height,width,C); % pixel standard deviations
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 = 1:length(source)
fr = source(n).cdata; % 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
% update gaussian components for each pixel
for i=1:height
for j=1:width
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
w(i,j,:) = w(i,j,:)./sum(w(i,j,:));
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];
% sort rank values
for k=2:C
for m=1:(k-1)
if (rank(:,:,k) > rank(:,:,m))
% swap max values
rank_temp = rank(:,:,m);
rank(:,:,m) = rank(:,:,k);
rank(:,:,k) = rank_temp;
% swap max index values
rank_ind_temp = rank_ind(m);
rank_ind(m) = rank_ind(k);
rank_ind(k) = rank_ind_temp;
end
end
end
% calculate foreground
match = 0;
k=1;
fg(i,j) = 0;
while ((match == 0)&&(k<=M))
if (w(i,j,rank_ind(k)) >= thresh)
if (abs(u_diff(i,j,rank_ind(k))) <= D*sd(i,j,rank_ind(k)))
fg(i,j) = 0;
match = 1;
else
fg(i,j) = fr_bw(i,j);
end
end
k = k+1;
end
end
end
figure(1),subplot(3,1,1),imshow(fr)
subplot(3,1,2),imshow(uint8(bg_bw))
subplot(3,1,3),imshow(uint8(fg))
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

Sign in to comment.


Dima Lisin
Dima Lisin on 17 Apr 2016
Edited: Dima Lisin on 17 Apr 2016
Use vision.ForegroundDetector from the Computer Vision System Toolbox.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!