Hello,
The problem of frame to frame automous tracking that you're talking about is a interesting one. Here is a paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.49.599&rep=rep1&type=pdf in how it's used for cars. I encourage you to hop on google scholar and search "track association in computer vision".
For your application, you'll need to balance the interpolation of the position in the future, with a confident fit of your curreny data. What I mean, is if you're assuming the frame to frame measures are linear(assuming the object is going to move a certain number of pixels in your image frame from a given time step), you'll need to associate them together with a linear regressor. If this linear regressor does not have a strong correlation coefficient, your track association may be weak and interpolations of future data may be poor.
You'll also need to segment what's detected in your image frame, if you have multiple things that require track association. This increased the computing burden a lot because we now need to find to optimal linear tracks for two objects. I'll leave the segmentation to you(i'll propose one idea here, but if you're unfamiliar with deep learning it may be a little much). PLease note that this code is just a framework for you to start, not a whole solutions, you'll need to run it and fill in some blanks for your application.
image=readimage('path/to/image.jpg');
image=rescale(image,0,1); %helps in binarization
BW_im=imbinarize(image);
jj=regionprops(BW_im,{'Centroid','MajorAxisLength','MinorAxisLength'});
%you say a bunch of white circles are here so let's make a SOM(self organising map)
%to segment them as best as possible.
%transform struct array jj to matrix
states=[cat(1,jj.Centroid),cat(1,jj.MajorAxisLength),cat(1,jj.MinorAxisLength)];
%define the SOM
dimension1 = som_size; %dimensions can be different
dimension2 = som_size;
net = selforgmap([dimension1 dimension2]);
%train the SOM
net.trainParam.showWindow = 0; %make ==1 if you wan't to see it
[net,tr] = train(net,states(:,1:4)');
outputs = net(states(:,1:4)');
%output vector of SOM
classes = vec2ind(outputs);
classified_outputs=[states,classes'];
%so we now have classed for all objects, let's separate them
[ff,~]=size(states);
for i=1:ff
if classified_outputs(end)==i
data_vault(i).centroid(end+1,:)=classified_outputs(i,1:2) %initialize this variable somewhere
end
end
So you now have sorted data to make some interpolations.
Hope this helps
RC
