Wow. I just took some time today to write benchmarking code and well, I wasn't expecting that much performance increase. Some weeks ago I have switched the similarity measure of the tracking tool from sum of absolute difference (AForge template matching) to Normalized cross correlation (as per the litterature it seem to be one of the best measure for template matching). I had spend quite some time to optimise my Normalized cross correlation code to be able to match the original speed. (because obviously there are much more operations involved when performing a normalized cross correlation than when doing the simple sum of differences between each pixel.) And today I benchmarked OpenCV normalized cross correlation (through EmguCV) against both. Here are some typical results, template was 20×20, search zone 100×100, and ran on 25 images in a loop : - Sum of absolute difference (AForge): 3129 ms. - Normalized cross correlation (joan): 2178 ms. - Sum of squared difference (OpenCV): 237 ms. - Normalized cross correlation (OpenCV): 238 ms. Yes, that's roughly 10 times faster. Resulting matches are the same. What does this mean ? Well, it means we'll be able to do multiple point tracking in real time AND use bigger search windows at the same time ! Awesome :-) Caveat: Now that I've seen this I can't go back. This performance increase HAS to be included in the next official release :-) joan.