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ramdas

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I am working on a project which is based on importance of

Now ,I have detected vehicles from the Video of Traffic on road taken using stationary camera ( Please download the 1.47 MB video for testing MATLAB Code by ( step1)

If you run the code in MATLAB, you can observe that I am quite successful in detecting all the vehicles in each video frames. But now I want to do tracking of

**phase only reconstruction of a signal obtained from fft**.Now ,I have detected vehicles from the Video of Traffic on road taken using stationary camera ( Please download the 1.47 MB video for testing MATLAB Code by ( step1)

**click on the play button**then (step2) right clicking on video then ( step3 ) click on save as option )If you run the code in MATLAB, you can observe that I am quite successful in detecting all the vehicles in each video frames. But now I want to do tracking of

**only one**vehicle with changes in my code.So can anybody help me how to detect**single**vehicle by doing changes in my MATLAB Code ?
Matlab:

```
tic
clc;
clear all;
close all;
%read video file
video = VideoReader('D:\dvd\Matlab code\test videos\5.mp4');
T= video.NumberOfFrames ; %number of frames%
frameHeight = video.Height; %frame height
frameWidth = video.Width ; %frameWidth
get(video); %return graphics properties of videoi=1;
for t=300:15:550 %select frames between 300 to 550 with interval of 15 from the video
frame_x(:,:,:,i)= read(video, t);
frame_y=frame_x(:,:,:,i);
%figure,
%imshow(f1),title(['test frames :' num2str(i)]);
frame_z=rgb2gray(frame_y); %convert each colour frame into gray
frame_m(:,:,:,i)=frame_y; %Store colour frames in the frame_m array
%Perform Gaussian Filtering
h1=(1/8)*(1/8)*[1 3 3 1]'*[1 3 3 1] ; % 4*4 Gaussian Kernel
convn=conv2(frame_z,h1,'same');
g1=uint8(convn);
Filtered_Image_Array(:,:,i)=g1; %Store filtered images into an array
i=i+1;
end
%Apply 3-D Fourier Transform on video sequences
f_transform=fftn(Filtered_Image_Array);
%Compute phase spectrum array from f_transform
phase_spectrum_array =exp(1j*angle(f_transform));
%Apply 3-D Inverse Fourier Transform on phase spectrum array and
%reconstruct the frames
reconstructed_frame_array=(ifftn(phase_spectrum_array));k=i;
i=1;
for t=1:k-1
%Smooth the reconstructed frame of Î(x, y, n) using the averaging filter.
Reconstructed_frame_magnitude=abs(reconstructed_frame_array(:,:,t));
H = fspecial('disk',4);
circular_avg(:,:,t) = imfilter(Reconstructed_frame_magnitude,H);
%Convert the current frame into binary image using mean value as the threshold
mean_value=mean2(circular_avg(:,:,t));
binary_frame = im2bw(circular_avg(:,:,t),1.6*mean_value); %Perform Morphological operations
se = strel('square',3);
morphological_closing = imclose(binary_frame,se);
morphological_closing=imclearborder(morphological_closing); %clear noise present at the borders of the frames %Superimpose segmented masks on it's respective frames to obtain moving
%objects
moving_object_frame = frame_m(:,:,:,i);
moving_object_frame(morphological_closing) = 255;
figure,
imshow(moving_object_frame,[]), title(['Moving objects in Frame :' num2str(i)]);
i=i+1;
end
toc
```

Last edited: