How to improve detection of Probalistic Hough

 

I'm trying to detect a clock hand in a video of an analogue display and extract the value it points to. I'm using Python with OpenCV for this.

What I essentially do is:

  • I'm using a Gaussian Blur to lower the noise in the current image.
  • I use Canny Edge Detection to filter the edges
  • I apply the probalistic Hough Line Transform

    • The Code for it: 

    def detect_clock_hand(img, center):
    # Convert to gray
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    # Apply gaussian blur
    blur = cv2.GaussianBlur(gray, (11, 11), 0, 0)

    # Apply sobel edge detection
    edges = cv2.Canny(blur, 30, 40)

    # Apply HoughTransform
    lines = cv2.HoughLinesP(edges, 10, np.pi / 180, 5, 15, 50)

    #Filter lines in a given radius
    filtered_edges = util.filter_edges(lines, center)

    I played a lot with the parameters to improve the results. The current status looks like this:

  • Canny 谨慎的

  • Hough HoughLineTransformP

    • As you can see, the result just marks a part of the clockhand. And that part isn't event a 'good' part from what I can tell from the Canny Edge Detection. I'm wondering if I use the Hough Transform wrong or the Edge Detection actually isn't as good as I think it is. Since I'm new to Image Processing, I'd be greatful for any advice.

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