I’m trying to detect a shape (a cross) in my input video stream with the help of OpenCV. Currently I’m thresholding to get a binary image of my cross which works pretty good. Unfortunately my algorithm to decide whether the extracted blob is a cross or not doesn’t perform very good. As you can see in the image below, not all corners are detected under certain perspectives.
I’m using findContours() and approxPolyDP() to get an approximation of my contour. If I’m detecting 12 corners / vertices in this approximated curve, the blob is assumed to be a cross.
Is there any better way to solve this problem? I thought about SIFT, but the algorithm has to perform in real-time and I read that SIFT is not really suitable for real-time.
I have a couple of suggestions that might provide some interesting results although I am not certain about either.
If the cross is always near the center of your image and always lies on a planar surface you could try to find a homography between the camera and the plane upon which the cross lies. This would enable you to transform a sample image of the cross (at a selection of different in plane rotations) to the coordinate system of the visualized cross. You could then generate templates which you could match to the image. You could do some simple pixel agreement tests to determine if you have a match.
Alternatively you could try to train a Haar-based classifier to recognize the cross. This type of classifier is often used in face detection and detects oriented edges in images, classifying faces by the relative positions of several oriented edges. It has good classification accuracy on faces and is extremely fast. Although I cannot vouch for its accuracy in this particular situation it might provide some good results for simple shapes such as a cross.