查找两个视频之间匹配的SIFT特征

我从两个视频中提取了SIFT特征以进行匹配。 我需要将每个第二视频特征与存储在第一视频的特征阵列中的特征进行比较。我在设置代码时遇到了问题,这样当我有了对应关系时,我就可以得到该功能所在的帧。我该怎么做?有人能给我看一个代码示例吗?

这是我的代码:

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