Camshift Tracking algorithm

Camshift stands for "Continuously Adaptive Mean Shift."
It has the basic Mean shift algorithm with the difference of a window that changes in size.

Pro: This method is fast and appears on initial testing to be moderately accurate. It may be possible to improve accuracy by using a different color representation.

Con: There are quite a few parameters: the number of histogram bins, the minimum saturation, minimum and maximum intensity, and the width-to-height ratio for regions. There's also a parameter for enlarging the region while doing Mean Shift to increase the chances of finding the maximum for probability density.



using original code from (Isaac Gerg, Adam Ickes, Jamie McCulloch) "http://www.gergltd.com/cse486/project5/" with some minor changes/refactoring done by me.

Tracking w/ blob detection, morphological operation (Togeather)

frames = {avi.cdata}; %uses the cdata from the video file

fg = extractForeground(frames); % do foreground extraction
cmap = colormap(gray);

for i = 1:length(fg)
temp0{i} = edge(fg{i}, 'canny', 0.99) + fg{i};
temp2 = temp0{i};
temp2 = cat(3,temp2,temp2,temp2);

fgs = rgb2gray(temp2);
sedisk = strel('square',10);
fgs = imclose(fgs, sedisk);
fgs = imfill(fgs,'holes');
RLL = bwlabel(fgs);

stats = regionprops(RLL,'basic','Centroid');
fig = figure(1),imshow(RLL)
hold on

for n = 1:length(stats)
if(stats(n).Area > 100)
plot(stats(n).Centroid(1), stats(n).Centroid(2),'r*')
end
end
hold off


end;

clear all;

Identify and track the center of the logical object

after morphological operation.
check if the area of blob is greater than a threshold

sedisk = strel('square',15);
fg = imclose(fg, sedisk);
fg = imfill(fg,'holes');
RLL = bwlabel(fg);

stats = regionprops(RLL,'basic','Centroid');
figure(1),imshow(fr_bw)
hold on

for n = 1:length(stats)
if(stats(n).Area > 100)
plot(stats(n).Centroid(1), stats(n).Centroid(2),'r*')
end
end

Problem: can track less 50% of the cars, however there are too many outiners because of the transformation done during stabilization. also hard to determine the area, i did it using trial and error to get the best fit

Object Tracking in a LIVE VIDEO STREAM

% %run first in command
% ONCE PER MATLAB SESSION
% vid = videoinput('winvideo', '1', 'YUY2_160x120');
% set(vid,'ReturnedColorSpace','rgb');
% set(vid,'TriggerRepeat',Inf);
% vid.FrameGrabInterval = 5;
% start(vid);
%------

figure;
while(vid.FramesAcquired<=1000) % Stop after 1000 frames data = getdata(vid,2); diff_im = imabsdiff(data(:,:,:,1),data(:,:,:,2)); %background subtraction
diff = rgb2gray(diff_im);
diff_bw = im2bw(diff,0.2);
bw2 = imfill(diff_bw,'holes');
s = regionprops(diff_im, 'centroid');
cd = s.Centroid;
centroids = cat(1, s.Centroid);
imshow(data(:,:,:,2));
hold(imgca,'on');
plot(imgca,centroids(:,1),centroids(:,2),'g*');

hold on;
rectangle('Position',[cd(:,1) cd(:,2) 20 20],'LineWidth',2,'EdgeColor','b');
hold(imgca,'off');

end

stop(vid)