index exceed array (the video file is mp4 of 144p)

clc

%%clear screen

video_name ='vish3.mp4'; %%video file

vid = VideoReader(video_name); %%reading the video file from the path

nframes = vid.NumberOfFrames; %defining number of frames

Height = vid.Height; %frame height

Width = vid.Width; %frame width

thr = 10; %threshold value(the mean 8-bit value of the original image)dividing into 2 parts background noise and foreground noise

%% main program kalman introduction

dt=10;

A = [1 0 dt 0;

0 1 0 dt; %%state transistion matrix as per dynamics of moving object

0 0 1 0 ;

0 0 0 1 ;

];

B =[(dt^2)/2, (dt^2)/2, dt, dt]';

u = 4e-3;

%We are observing the X, and Y position. So our observation is: y = [X Y]

H = [1 0 0 0;

0 1 0 0];

%covariance matrix calculations

State_Uncertainty = 10; %state variable are independent so it an diagonal matrix

S = State_Uncertainty * eye(size(A,1));

Meas_Uncertainty = 1;

R = Meas_Uncertainty * eye(size(H,1));

Dyn_Noise_Variance = (0.01)^2;%external force modeled as disturbance

%[(1/2)*(dt^2) (1/2)*(dt^2) dt dt 1 1]'; %Constant Acceleration

% [(1/2)*(dt^2) (1/2)*(dt^2) dt dt]'; %Constant Velocity

Q = [(dt^2)/4 0 (dt^3)/2 0;

0 (dt^2)/4 0 (dt^3)/2; %x and y are independent variable

(dt^3/2) 0 (dt^2) 0;

0 (dt^3)/2 0 (dt^2);

];

Input = []; %%variables

x = [];

Kalman_Output = []; %%initial values

x = [Height/2; Width/2; 0; 0;];

%background extraction

background_frame = BackgroundExt(video_name); %background frame based on the samples

%calculating the noise

moving = zeros(Height,Width,nframes);

labeled_frames = zeros(Height,Width,nframes);

bb=0;

for i=1:nframes-1

current_frame = double(read(vid,i));

moving(:,:,i) = (abs(current_frame(:,:,1) - background_frame(:,:,1)) > thr)...

|(abs(current_frame(:,:,2) - background_frame(:,:,2)) > thr)... %%calculating absolute value and threshold value for tracking noise

|(abs(current_frame(:,:,3) - background_frame(:,:,3)) > thr);

moving(:,:,i) = bwmorph(moving(:,:,i),'erode',2); %%morphing the moving image(eroding the image)

labeled_frames(:,:,i) = bwlabel(moving(:,:,i),4); %Label connected components in 2-D binary image

stats{i} = regionprops(labeled_frames(:,:,i),'basic');% measuring image property

[n_obj,features] = size(stats{i});

area = 0;

if(n_obj ~= 0)

for k=1:n_obj

if(stats{i}(k).Area > area)

id(i) = k;

area = stats{i}(k).Area;

end

end

centroid(:,:,i) = stats{i}(id(i)).Centroid;

else

centroid(:,:,i) = [rand*200 rand*200]; %%random values for centroid calculation

bb = bb+1;

end

i %indicates the frame number

end

for r=1:nframes-1 %pointing error

frames = read(vid,r);

frames = insertShape(frames,'circle',[centroid(1,1,r) centroid(1,2,r) sqrt(stats{r}(id(r)).Area/pi)],'LineWidth',1);

marked_noise(:,:,:,r) = frames;

end

%updating kalman filter

for r=1:nframes-1

frames = read(vid,r);

frames = insertShape(frames,'circle',[centroid(1,1,r) centroid(1,2,r) 4],'LineWidth',2);

if(mod(r,2) == 0) %%tracking the original object

input = [centroid(1,1,r); centroid(1,2,r)];

else

input=[];

end

x = A*x + B*u; %prediction(next state)

S = A*S*A' + Q; %%error covairance

K = S*H'*inv(H*S*H'+R);%correction(gain)

%updating the estimation\\

if(~isempty(input)) %Check if we have an input

x = x + K*(input - H*x);

end

S = (eye(size(S,1)) - K*H)*S; %update the covariance error

Kalman_Output = H*x; %plotting the measurement

frames = insertShape(frames,'circle',[Kalman_Output(1) Kalman_Output(2) 4],'LineWidth',2,'Color','black');

scenario_1(:,:,:,r) = frames;

r

end

implay(scenario_1,15); %%image output

[Bob Thompson]

Please post the entire error message.

[vishalkumar solanki]

Unable to perform assignment because the size of the left side is 360-by-480-by-3 and the size of the right side is 360-by-640-by-3.

Error in randommm (line 74)

marked_noise(:,:,:,r) = frames;

[Bob Thompson]

My first guess is that not all of the images in vid are consistently sized. I would double check that first.