Tracking user defined points with OpenCV

2
votes

I'm working on a project where I need to track two points in an image. So far, the best way I have of identifying these points is to get the user to click on them when the program is first run. I'm using the Lucas-Kanade Pyramid method built into OpenCV (documented here, but as is to be expected, this doesn't work too well. Is there a better alternative algorithm for tracking points in OpenCV, or alternatively some other way of verifying the points I already have?
I'm currently considering using GoodFeaturesToTrack, and getting the distance from each point to the one that I want to track, and maybe some sort of vector pointing out the relationship between the two points, and using this information to determine my new point. I'm looking for suggestions of ways to go about this, not necessarily code samples. Thanks

EDIT: I'm tracking small movements, if that helps

1
algorithmopencvlanguage-agnostictracking
LK Pyramid of OpenCV is quite good...upload some image of your results... - rotating_image
It's tracking the points fine, I'm just not choosing what would be considered "Good Points" Im sure. I'm well aware of the parameters and limitations of the function, I just wanted some way to verify the points that I'm tracking. - maccard

1 Answers

5
votes

If you look for a solution that is implemented in opencv the pyramidal Lucas Kanade (PLK) method is quit good, else I would prefer a Particle Filter based tracker. To improve your tracking performance with the PLK be sure that you have set up the parameters correctly. E.g. for large motion you need a level at ca. 3 or 4. The window should not be to small ( I prefer 17x17 to 27x27). Also keep in mind that the methods needs textured areas to be able to track the points. That means corner like image content (aperture problem).

I would propose to seed a set of points (ps) in a grid around the points (P) you want to track. And than use a foreward - backward threshold to reject falsly tracked points. The motion of your points (P) will be computed by the mean motion of the particular residual point sets (ps).

The foreward backward confidence is computes by estimating the motion from frame 1 to frame 2. (ptList1 -> ptList2). And that from frame 2 to frame 1 with the points of ptList2 (ptList2 -> ptListRef). Motion vectors will be rejected if (|| ptRef - pt1 || > fb_threshold).