I have created an experimental fast rectangular object tracking system; it will be used for headtracking and controllling objects in 3D engine (Ogre3D).
For now I am able to show to the webcam any kind of bright colored rectangle (text markers are good objects) and system registers basic properties of this object (hue/value/lightness and initial width and height in 0 degrees rotation).
After I have registered the trackable object, I do some simple frame processing to create grayscale probabilty map.
So now I have 2 known things:
1) 4 corners for the last object position (it’s always a rectangle but it may be rotated)
2) a pretty rectangular (but still far from perfect) blob which is the brightest in the frame. I can get coordinates of any point of the blob without problems, point detection is stable enough.
I can find a bounding rectangle of the object without problems, but I have a problem with detecting the object corners themselves.
I need the simplest possible (quick&dirty would be great) algorithm to scan the image starting with some known coordinates (a point inside the blob) and detect new 4 x,y coordinates of a “blobish” rectangle corners (not corners of a bounding box but corners of the rectangular blob itself).
Ready-to-use C++ function would be awesome, but somehow google doesn’t like me today 🙁
I think that it would be overkill to use some complicated function form OpenCV library just to extract 4 points of a single rectanglular blob. But if you know a quick and efficient way how to do it using OpenCV (it must be real-time and light on CPU because I’ll run the 3D engine at the same time) then I would be really grateful.
You can apply Hough transform on segmented image to detect lines. Using detected lines you can calculate their intersection to find the corner coordinates of the blob.