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meanshift termination criteria & epsilon value meaning

asked 2016-05-27 14:11:06 -0500

logidelic gravatar image

updated 2016-07-04 13:58:04 -0500

I'm wondering if someone could give an explanation of the epsilon value passed as part of the meanshift termination criteria. I've read the documentation of course, and searched around, but am still unsure of the meaning.

Motivation for this question: I am trying to determine the likelihood that what meanshift found is indeed the original object of interest (i.e. that the new rectangle is similar to the previous ROI). I was surprised that, sometimes, if my original object has left the frame, meanshift identified some background noise as the new location (which, to my eyes, had nothing in common with the original ROI).

Currently I'm using a max count of 10 and an epsilon of 1 for the termination criteria. I then ensure that the count returned from meanShift() is < 10, or else I discard the result. Still, it seems to terminate sooner, even when the object is gone...


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answered 2016-07-06 12:02:27 -0500

logidelic gravatar image

Would still love to hear other peoples' answers to this, but for the moment, I've added a simple histogram comparison (using compareHist) to try to get a sense of the similarity between the original and the tracked object. If the similarity is less than some threshold, I assume I've lost the track.

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Asked: 2016-05-27 14:11:06 -0500

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Last updated: Jul 06 '16