I need to find matching between two independent sets of features extracted from two images of the same scene captured by two different cameras. I'm using the HumanEvaI data set, so I have the calibration matrices of the cameras (in this particular case BW1 and BW2).
I can not use method like simple correlation, SIFT or SURF to solve the problem because the cameras are quite far away from each other and also rotated. So the differences between the images are big and there is occlusion as well.
I have been focused in finding an Homography between the captured images based on ground truth points matching that I have been able to build due to the calibration information I already have. Once I have this homography I will use a perfect matching (Hungarian algorithm) to find the best correspondence. The importance of the homography here is that is the way I have to calculate the distance between the points.
So far everything seems fine, my problem is that I haven't been able to find a good homography . I have tried RANSAC method, gold standard method with sampson distance (this is a nonlinear optimization approach) and mainly everything from a book called 'Multiple View Geometry in Computer Vision' Second Edition by Richard Hartley.
I have implemented everything in matlab so far.
Is there another way to do this? I'm I in the right path? If so what could I have been doing wrong?
Another method I think you might find useful is described here.
This approach tries to fit local models to group of points. Its global optimization method allows it to outperform RANSAC when several distinct local models exists.
I also believe they have code available.