Motion detection using kalman filter

My code dont run after a frame. Help me in fixing it. Here is the code........

           clear all; close all; clc

           %%Read video into MATLAB using aviread
            video = mmreader('r2.avi');
             nframes = length(video);
            a=read(video,1);
            % Calculate the background image by averaging the first 5 images
             temp = zeros(size(a));
           [M,N] = size(temp(:,:,1));
            vidHeight = video.Height;
            vidWidth = video.Width;
            mov(1:nframes) = ...
     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
imbkg = temp/10;

               % Initialization for Kalman Filtering
             centroidx = zeros(nframes,1);
             centroidy = zeros(nframes,1);
             predicted = zeros(nframes,4);
             actual = zeros(nframes,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:nframes
           %mov(i).cdata=read(video,i);
           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