[Greg McKaskle]

And what was the problem with dividing by the area? That will give score a range from 0 -- when no pixels match, to 1.0 when all pixels match. That normalizes the score.

I think the bigger problem is that your goal is to determine the shift, and counting matching pixels doesn't always act as a good predictor of shifted distance.

Lets look at two examples.
If the image is simple ... a black background and a smaller white square within it. If I shift the square one pixel to the right, two rows of pixels will contain errors. If I shift it two pixels four rows have errors. It seems to work well. Once the shift amount is greater than the edge size of the square, the score plateaus -- not that helpful in determining shift amount.

In the second image, lets have stripes, like a picket fence. The stripes are two pixels wide with two pixels of black in between. Shift one pixel and half of the rows have errors. Shift two and all rows have errors. Shift three and half have errors again, four and no errors (depends on what enters the frame).

Anyway this difficult problem, and a simple score like you are computing works well in some cases, but less well in others.

After you've completed this approach, perhaps you want to try another based on feature extraction. It could find the largest particle, or the ten largest and compare positions of those. It could identify corners within the image and track those. You may also find it useful to read about Optical Flow algorithms. They are pretty sophisticated to implement, but they are pretty robust.

Greg McKaskle