python - How to improve detection of Probalistic Hough-Lines-Transform?

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:

1. I'm using a Gaussian Blur to lower the noise in the current image.


2. I use Canny Edge Detection to filter the edges


3. 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:

1. Canny
   




2. Hough
   

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.