Object recognition performance with ORB

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I have a sample project where I am trying to recognize, and draw a box around, a circuit board. Ideally I want the result to be something close to this: https://www.youtube.com/watch?v=-ZNYo... I'm hoping that some people with more experience and intution can suggest changes to my algorithm and code to improve results.

I am using OpenCV with Python, and have been playing around with the ORB detector, which I understand is a free license vs. SIFT or SURF. Also it seems fairly efficient. However my code isn't working too well for matching. It runs fast in real-time, but below are some sample match attempts:

On the left is my (static) template, on the right is the real-time scene. I've taken a few screenshots that are indicative of my results: not that many matches, some mismatches, and it's never able to draw a nice bounding box around the object as my code would (hopefully) have it do. I've also tried taking more vertical/birds-eye pictures of the template, but with similarly poor results. Resolution for template and image are both 320x240.

Below I will paste my code. It uses the ORB recognizer as I mentioned. I am open to switching approaches/algorithms but I'd also like to understand why I could expect better performance if doing so. Thank you in advance for any help or advice, hopefully others will benefit as well.

def ORB_recognizer(detector, kp1, des1, img2):

    ms1=0;ms2=0;ms3=0;ms4=0;ms5=0;ms6=0;ms7=0;ms8=0 # I was profiling performance

    ms1 = time.time()*1000.0

    kp2, des2 = detector.detectAndCompute(img2,None)

    ms2 = time.time()*1000.0

    # create BFMatcher object
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    # Second param is boolean variable, crossCheck which is false by default. If it is true, Matcher returns only those matches with value (i,j) such that i-th descriptor in set A has j-th descriptor in set B as the best match and vice-versa.
    # Match descriptors.

    ms3 = time.time()*1000.0

    matches = bf.match(des1,des2)

    ms4 = time.time()*1000.0

    # Sort them in the order of their distance.
    matches = sorted(matches, key = lambda x:x.distance)

    MIN_MATCH_COUNT = 10
    good = matches[:MIN_MATCH_COUNT]    

    #if len(good)>MIN_MATCH_COUNT:

    ms5 = time.time()*1000.0

    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()

    ms6 = time.time()*1000.0

    h,w,d = img1.shape
    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,255,3, cv2.LINE_AA)

    ms7 = time.time()*1000.0
    #else:
        #print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
        #matchesMask = None




    draw_params = dict(matchColor = (0,255,0), # draw matches in green ...