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...

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shubha p
shubha p on 1 Dec 2015
Edited: Thorsten on 1 Dec 2015
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

Answers (1)

Dima Lisin
Dima Lisin on 1 Dec 2015
You may want to try vision.KalmanFilter object that comes with the Computer Vision System Toolbox. See this example.

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