Improved object tracking

So it turns out that L*a*b* image differencing was implemented improperly. Among other things, the limited precision of the default OpenCV types made objects appear when they shouldn't. As a result, it looked like RGB differencing performed better. I made the necessary changes to fix the tracker, resulting in this video.

In particular, the L*a*b* image differencing now takes the absolute difference of all three channels, adds them together and then smooths the result using a 7x7 Gaussian kernel. The tracking now performs slightly better than the RGB version, but judging from the results, blob merging will actually be needed after all. From what it looks like, the white blobs that appear in the difference video aren't strong enough to be detected as contiguous blobs. Thresholding, using OpenCV, results in either lots of unintended noise or nothing at all. Reducing the blob detection threshold further than we already have (it's now 25, down from 100) should produce better results.

Also, Daniel and I went out to AP&M to perform foreshortening calculations and data collection. We were able to take video from both Glen Tesler's office and from the bridge between both sections of AP&M. This resulted in the following videos:

AP&M
AP&M (2)
AP&M (3)
AP&M (4)
AP&M (5)
AP&M (6)

[The videos that are only a second or so long are intended as snapshots of Daniel on the lawn in front of AP&M]