Find SIFT features matching between two video

Question

I extracted SIFT features from two videos to make a matching. I need to compare each of the second video features with those stored in an array of features of the first video. I'm having trouble set the code so that when I have the correspondence I can get the frame in which the feature is present. How can I do? Can anyone show me a code example?

This is my code:

obj = VideoReader('video2.avi');
lastFrame = read(obj, inf);
numFrames = obj.NumberOfFrames;

%estrazione frame e sift
for k = 1 : 3 % numFrames / 5
    disp(['Processing frame #', num2str(k)]);
    this_frame = read(obj, k * 5); % leggi solo un fotogramma ogni 5 per velocizzarle la cosa
    this_frame = imresize(this_frame, 0.5); % rimpiccioliamolo per questioni di efficienza!
    I = single(rgb2gray(this_frame)) ;
    [f,d] = vl_sift(I);  % estrazione feature

    features{k} = f;     % salviamo le feautre e i relativi descrittori in delle celle
    descriptors{k} = d;
end
save('feature_input', 'features');
save('descrittori_input', 'descriptors');

%%% un esempio di come ripescare i dati...
pippo = load('feature_input');
newfeat = pippo.features;
pippo = load('descrittori_input');
newdesc = pippo.descriptors;

for k = 1 : 3
    disp(['Le feature del fotogramma', num2str(k), ' sono: ']);
    f = cell2mat( newfeat(k) );
    f(:, 1:10) % ne mostriamo solo un pezzetto... le posizioni delle prime 10 features
end


obj2 = VideoReader('video2u.avi');
lastFrame = read(obj2, inf);
numFrames = obj2.NumberOfFrames;



%estrazione frame e sift video2
for k2 = 1 : 3 % numFrames / 5
    disp(['Processing frame #', num2str(k2)]);
    this_frame2 = read(obj2, k2 * 5); % leggi solo un fotogramma ogni 5 per velocizzarle la cosa
    this_frame2 = imresize(this_frame2, 0.5); % rimpiccioliamolo per questioni di efficienza!
    K = single(rgb2gray(this_frame2)) ;
    [f2,d2] = vl_sift(K);  % estrazione feature

    features2{k2} = f2;     % salviamo le feautre e i relativi descrittori in delle celle
    descriptors2{k2} = d2;
end




save('feature2_input', 'features2');
save('descrittori2_input', 'descriptors2');

%%% un esempio di come ripescare i dati...
pippo2 = load('feature2_input');
newfeat2 = pippo2.features2;
pippo2 = load('descrittori2_input');
newdesc2 = pippo2.descriptors2;

for k2 = 1 : 3
    disp(['Le feature del fotogramma', num2str(k2), ' sono: ']);
    f2 = cell2mat( newfeat2(k2) );
    f2(:, 1:10) % ne mostriamo solo un pezzetto... le posizioni delle prime 10 features


end


[matches, scores] = vl_ubcmatch(d, d2, 1.5) ;

% sift points plot

    subplot(1,2,1);
    imshow(uint8(I));
    hold on;
    plot(f(1,matches(1,:)),f(2,matches(1,:)),'b*');


    subplot(1,2,2);
    imshow(uint8(K));
    hold on;
    plot(f2(1,matches(2,:)),f2(2,matches(2,:)),'r*');


    figure;

     %-------------  

 % RANSAC

X1 = f(1:2,matches(1,:)) ; X1(3,:) = 1 ;
X2 = f2(1:2,matches(2,:)) ; X2(3,:) = 1 ;


numMatches = size(matches,2) ;

for t = 1:100
  % estimate homograpyh
  subset = vl_colsubset(1:numMatches, 4) ;
  A = [] ;
  for i = subset
    A = cat(1, A, kron(X1(:,i)', vl_hat(X2(:,i)))) ;
  end


  [U,S,V] = svd(A) ;


H{t} = reshape(V(:,9),3,3) ;

  % score homography
  X2_ = H{t} * X1 ;
  du = X2_(1,:)./X2_(3,:) - X2(1,:)./X2(3,:) ;
  dv = X2_(2,:)./X2_(3,:) - X2(2,:)./X2(3,:) ;
  ok{t} = (du.*du + dv.*dv) < 6*6 ;
  score(t) = sum(ok{t}) ;
end



[score, best] = max(score) ;
H = H{best};
ok = ok{best};


% sift feature matching 

   dh1 = max(size(K,1)-size(I,1),0) ;
   dh2 = max(size(I,1)-size(K,1),0) ;


subplot(2,1,1) ;
imagesc([padarray(I,dh1,'post') padarray(K,dh2,'post')]) ;
 colormap (gray);
o = size(I,2) ;
line([f(1,matches(1,:));f2(1,matches(2,:))+o], ...
     [f(2,matches(1,:));f2(2,matches(2,:))]) ;


axis image off ;

subplot(2,1,2) ;
imagesc([padarray(I,dh1,'post') padarray(K,dh2,'post')]) ;
 colormap (gray);
o = size(I,2) ;
line([f(1,matches(1,ok));f2(1,matches(2,ok))+o], ...
     [f(2,matches(1,ok));f2(2,matches(2,ok))]) ;
title(sprintf('%d (%.2f%%) inliner matches out of %d', ...
              sum(ok), ...
              100*sum(ok)/numMatches, ...
              numMatches)) ;
axis image off ;

drawnow ;

end

Answer 1

You are matching using [matches, scores] = vl_ubcmatch(d, d2, 1.5) ;. d contains only the descriptors of the latest frame. You should do something like:

for nFrames=1:3
   [matches{nFrames}, scores{nFrames}] = vl_ubcmatch(descriptors{nFrames}, descriptors2{nFrames}, 1.5);
end

From here, you should be able to get the matches between the frames.