Motion detection using kalman filter- my code is not running after a frame. Help me in fixing it. code i have taken from matlab. but i changed the video only. here is my code...

clear all; 
close all; 
clc 
%%Read video into MATLAB using aviread
%video = aviread('rhinos.AVI');
%video = aviread('traffic.avi');
%video = aviread('test2.avi');
%video = mmreader('traffic.avi');

video = mmreader('traffic.avi');

% read all video frames
frames = length(video);

% moviename = ['test2_new.mov']; fps = 12;
 %video = avifile(moviename,'fps',fps,'compression','none');

%'n' for calculating the number of frames in the video file
n = length(video);

a=read(video,1);

% Calculate the background image by averaging the first 10 images
%temp = zeros(size(video(1).cdata));
 temp = zeros(size(a));
           [M,N] = size(temp(:,:,1));
            vidHeight = video.Height;
            vidWidth = video.Width;
           mov(1:frames) = ...
     struct('cdata', zeros(vidHeight, vidWidth, 3, 'uint8'),...
          'colormap', []);
for i = 1:10 
    mov(i).cdata=read(video,i);
    %imshow(mov(i).cdata);
    temp = double(mov(i).cdata) + temp;
end

%[M,N] = size(temp(:,:,1));
% for i = 1:10 
%     temp = double(video(i).cdata) + temp;
% end
imbkg = temp/10; 
% Initialization step for Kalman Filter
centroidx = zeros(n,1);
centroidy = zeros(n,1);
predicted = zeros(n,4);
actual = zeros(n,4); 
% % Initialize the Kalman filter parameters
% R - measurement noise,
% H - transform from measure to state
% Q - system noise,
% P - the status covarince matrix
% A - state transform matrix

R=[[0.2845,0.0045]',[0.0045,0.0455]'];
H=[[1,0]',[0,1]',[0,0]',[0,0]'];
Q=0.01*eye(4);
P = 100*eye(4);
dt=1;
A=[[1,0,0,0]',[0,1,0,0]',[dt,0,1,0]',[0,dt,0,1]']; 
% loop over all image frames in the video
kfinit = 0;
th = 38;

for i=1:n
imshow(mov(i).cdata);
hold on
imcurrent = double(mov(i).cdata);

% Calculate the difference image to extract pixels with more than 40(threshold) change
diffimg = zeros(M,N); 
diffimg = (abs(imcurrent(:,:,1)-imbkg(:,:,1))>th) ...
    | (abs(imcurrent(:,:,2)-imbkg(:,:,2))>th) ...
    | (abs(imcurrent(:,:,3)-imbkg(:,:,3))>th); 
% Label the image and mark
labelimg = bwlabel(diffimg,4);
markimg = regionprops(labelimg,['basic']);
[MM,NN] = size(markimg); 
% Do bubble sort (large to small) on regions in case there are more than 1
% The largest region is the object (1st one)
for nn = 1:MM
    if markimg(nn).Area > markimg(1).Area
        tmp = markimg(1);
        markimg(1)= markimg(nn);
        markimg(nn)= tmp;
    end
end 
% Get the upper-left corner, the measurement centroid and bounding window size
bb = markimg(1).BoundingBox;
xcorner = bb(1);
ycorner = bb(2);
xwidth = bb(3);
ywidth = bb(4);
cc = markimg(1).Centroid;
centroidx(i)= cc(1);
centroidy(i)= cc(2);

% Plot the rectangle of background subtraction algorithm -- blue
hold on
rectangle('Position',[xcorner ycorner xwidth ywidth],'EdgeColor','b');
hold on
plot(centroidx(i),centroidy(i), 'bx'); 
% Kalman window size
kalmanx = centroidx(i)- xcorner;
kalmany = centroidy(i)- ycorner; 
if kfinit == 0
    % Initialize the predicted centroid and volocity
    predicted =[centroidx(i),centroidy(i),0,0]' ;
else
    % Use the former state to predict the new centroid and volocity
    predicted = A*actual(i-1,:)';
end

   kfinit = 1; 
  Ppre = A*P*A' + Q;
  K = Ppre*H'/(H*Ppre*H'+R);
  actual(i,:) = (predicted + K*([centroidx(i),centroidy(i)]' - H*predicted))';
  P = (eye(4)-K*H)*Ppre; 
  % Plot the tracking rectangle after Kalman filtering -- red
  hold on
rectangle('Position',[(actual(i,1)-kalmanx) (actual(i,2)-kalmany) xwidth ywidth], 'EdgeColor', 'r','LineWidth',1.5);
  hold on
  plot(actual(i,1),actual(i,2), 'rx','LineWidth',1.5);
  drawnow;
end

Answer 1

[Dima Lisin]

You may want to try vision.KalmanFilter object that comes with the Computer Vision System Toolbox. See this example.