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

Low complexity background subtraction using frame difference method

Frame differencing, also known as temporal difference, uses the video frame at time t-1 as the background model for the frame at time t. This technique is sensitive to noise and
variations in illumination, and does not consider local consistency
properties of the change mask.
This method also fails to segment the non-background objects if they stop moving. Since it uses only a single previous frame, frame differencing may not be able to identify the interior
pixels of a large, uniformly-colored moving object. This is commonly known as the aperture problem.


a major flaw of this method is that for objects with uniformly distributed intensity values, the pixels are interpreted as part of the background. Another problem is that objects must be continuously moving. If an object stays still for more than a frame period (1/fps), it becomes part of the background.
This method does have two major advantages. One obvious advantage is the modest computational load. Another is that the background model is highly adaptive. Since the background is based solely on the previous frame, it can adapt to changes in the background faster than any other method (at 1/fps to be precise). As we'll see later on, the frame difference method subtracts out extraneous background noise (such as waving trees), much better than the more complex approximate median and mixture of Gaussians methods.
A challenge with this method is determining the threshold value.

The video is a result of stabilization using SIFT features.

Code:

clear all;close all;clc;
source = aviread('stabilized');
thresh = 40;
bg = source(1).cdata; % read in 1st frame as background frame
bg_bw = rgb2gray(bg); % convert background to greyscale
% ----------------------- set frame size variables -----------------------
fr_size = size(bg);
width = fr_size(2);
height = fr_size(1);
fg = zeros(height, width);
% --------------------- process frames -----------------------------------
for i = 2:length(source)
fr = source(i).cdata; % read in frame
fr_bw = rgb2gray(fr); % convert frame to grayscale
fr_diff = abs(double(fr_bw) - double(bg_bw));
for j=1:width
for k=1:height
if ((fr_diff(k,j) > thresh))
fg(k,j) = fr_bw(k,j);
else
fg(k,j) = 0;
end
end
end
bg_bw = fr_bw;
figure(1),subplot(3,1,1),imshow(fr)
subplot(3,1,2),imshow(fr_bw)
subplot(3,1,3),imshow(uint8(fg))
end