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

  0 Comments

Sign in to comment.

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.

  0 Comments

Sign in to comment.


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

Show 1 older comment
Image Analyst
Image Analyst on 21 Mar 2016
You forgot to post a picture. Usually when people ask for image processing advice they post a picture.
sapphiregoh
sapphiregoh on 16 Apr 2016
Hmm..how about i'm exracting each video frames and apply gaussian formula?
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.

  0 Comments

Sign in to comment.


Translated by