performance of findHomography
I am trying to detect a planar object from a video stream. Keypoint detection and feature extraction work both fairly well, as well as matching. findHomography however takes very long (up to 1,6 sec on my mobile). I tried several combinations of detector/extractor/matcher to change the number and quality of keypoints. My current setup is ORB and BFMatcher<Hamming>. My question is: How can I speed up the homography calculation? I'm using ransac, but changing the threshold seems to have little to no effect (neither in quality nor performance). Should I use some other methods like getPerspectiveTransform, or estimateAffine3D or solvePnp?
On a side note: I also had a hard time to get a useful matrix out of findHomography in the first place, although I sticked pretty close to opencv's samples.
How many points do you have in each set of keypoints?
The object image has about 260 keypoints with a resolution of 327x245 px and the scene images (camera frames) are usually between 460 to 490 with 352x288 res. They give me usually 260 matches. Another thing I might add is the fact, that the tracked object is expected to appear about 2 to 3 times smaller in the scene image than in its reference image. So in the scene image there are obviously less keypoints on the object itself than on some background clutter. So I guess I have to get rid of some bad matches first, before calculating the homography?