Dear all,
I've been testing numerous methods for helipad tracking (H in a circle) but none seems to be ideal for the task.
Appreciate anyone who could enlighten me on a reliable approach for tracking such an object, or any way to improve my current ORB parameters.
What I tried:
1. Colour + Contour Finding: Works most of the time with a downward facing camera but prone to environmental noise giving false contour matches (finding a shape with 12 edges), not exactly skew invariant since some edges disappears at an angle.
2. ORB/SIFT: Too many false positive matches due to the generic shape of the landing pad.
MIN_MATCH_COUNT = 10
img1 = cv2.imread('helipad.jpg',0)
img2 = gray
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1,des2)
dmatches = sorted(matches, key = lambda x:x.distance)
src_pts = np.float32([kp1[m.queryIdx].pt for m in dmatches]).reshape(-1,1,2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in dmatches]).reshape(-1,1,2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
h,w = img1.shape[:2]
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
dst = cv2.perspectiveTransform(pts,M)
img2 = cv2.polylines(img2, [np.int32(dst)], True, (0,0,255), 1, cv2.LINE_AA)
cv2.imshow("Tracker", img2)
res = cv2.drawMatches(img1, kp1, img2, kp2, dmatches[:20],None,flags=2)
cv2.imshow("Match", res)