I am trying to detect the starbucks logo in the scene. How to get rid of these artifacts in bounding polylines?

Question

I built a starbucks logo detector, but I get these strange artifacts when I draw the polylines that should surround the logo.

Here is the correct result:

Here are some examples of artifacts:

I use SIFT to detect key points and draw a rectangle as described in OpenCV tutorials, as shown here :

 import numpy as np
 import cv2

 cap = cv2.VideoCapture(0)

 sift = cv2.xfeatures2d.SIFT_create()
 img1 = cv2.imread('logo.png', 0)
 img1.resize(512, 512)
 kp1, des1 = sift.detectAndCompute(img1, None)
 while (True):
     ret, frame = cap.read()
     frame = findLogo(frame, kp1=kp1, des1=des1)
     cv2.imshow("frame",frame)
     if cv2.waitKey(1) & 0xFF == ord(' '):
         break
 cap.release()
 out.release()
 cv2.destroyAllWindows()


 def findLogo(frame, kp1, des1):
     MIN_MATCH_COUNT = 10
     # Initiate SIFT detector
     sift = cv2.xfeatures2d.SIFT_create()

     img2 = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     # find the keypoints and descriptors with SIFT
     kp2, des2 = sift.detectAndCompute(img2, None)
     if len(kp2) != 0 and des2 is not None:
         FLANN_INDEX_KDTREE = 0
         index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
         search_params = dict(checks=50)

         flann = cv2.FlannBasedMatcher(index_params, search_params)

         matches = flann.knnMatch(des1, des2, k=2)

         # store all the good matches as per Lowe ratio test.
         good = []
         for m, n in matches:
             if m.distance < 0.7 * n.distance:
                 good.append(m)
         if len(good) > MIN_MATCH_COUNT:
             src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
             dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

             M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

             matchesMask = mask.ravel().tolist()

             # h, w = img1.shape
             h = 512
             w = 512
             pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
             if M is not None:
                 dst = cv2.perspectiveTransform(pts, M)
                 frame = cv2.polylines(frame, [np.int32(dst)], True, (0, 255, 255), 3, cv2.LINE_AA)

         else:
             print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))
             matchesMask = None 
     return frame

I see that they occur when the program cannot detect the image. This program has lines to prevent this (and it works most of the time, for example, when there is nothing on the screen), but this error still occurs. Changing MIN_MATCH_COUNTto a larger number did not help. As you can see here:

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