Developing a Golf Analysis Software to Track Rotation Points on Golf Clubs

Tracking the rotation point along a golf club during a golfer’s pre-swing movements is essential for improving performance and refining technique. By developing a golf analysis software, this critical data can be captured automatically using standard 2D cameras and advanced video tracking algorithms.

This guide will walk you through the process of developing such a software, incorporating both practical and technical considerations to ensure a reliable and efficient solution.

Step 1: Why Track the Rotation Point?

The rotation point is the location on the golf club where the movement pivots before a swing begins, dividing the club into two sections. Identifying this point is crucial for coaches and players alike, as it provides insight into:

• Posture and grip.
• Pre-swing habits.
• Customized coaching based on a golfer’s unique movements.

By automating the detection of this rotation point, the software helps golfers and coaches make data-driven decisions to improve performance.

Step 2: Technologies and Tools Required

To develop a robust golf analysis software, several technologies and tools will be used:

1. Python: A flexible programming language for handling video processing and computer vision tasks.
2. OpenCV (Open Source Computer Vision Library): This is one of the most popular libraries for video processing and motion detection. OpenCV allows us to track club movements and detect the rotation point by analyzing video frames.
3. MediaPipe: A Google-developed framework that offers pre-built machine learning solutions for pose tracking and motion analysis. It can be used to enhance the precision of movement detection, especially when tracking subtle club movements.
4. NumPy: Used for performing numerical operations on video frames, allowing for faster calculations when detecting the club’s movement and rotation point.

Step 3: Detecting the Rotation Point Using OpenCV and Video Tracking

The core of the application involves using 2D cameras to record a golfer’s pre-swing movements. The software will then use OpenCV to process each frame and detect the rotation point of the golf club.

Here’s a high-level breakdown of the process:

1. Video Input: Capture video of the golfer’s pre-swing using standard 2D cameras.
2. Tracking Markers: Markers placed on the golf club will help the software detect the club’s movements accurately.
3. Detecting the Rotation Point: The software analyzes the club’s movement frame-by-frame, identifying the rotation point based on positional changes of the markers.

Here’s a simple example of how OpenCV can be used to detect the golf club and its rotation point:

python

import cv2

import numpy as np

# Load video

cap = cv2.VideoCapture(‘golf_swing.mp4’)

while cap.isOpened():

ret, frame = cap.read()

if not ret:

break

# Convert frame to grayscale for better detection

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# Use thresholding to detect the markers

_, thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)

# Find contours of the markers

contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Draw bounding boxes around detected markers

for contour in contours:

x, y, w, h = cv2.boundingRect(contour)

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

# Display the frame

cv2.imshow(‘Golf Analysis’, frame)

if cv2.waitKey(1) & 0xFF == ord(‘q’):

break

cap.release()

cv2.destroyAllWindows()

This code demonstrates how the software can detect and track movement markers on the club, helping pinpoint the rotation point.

Step 4: Handling Complex Movements and Accuracy

Golf club movements during the pre-swing can vary significantly, from quick and abrupt to subtle and nearly invisible shifts. To handle these diverse movement patterns, MediaPipe can be integrated to enhance accuracy. MediaPipe’s machine learning models can detect even the smallest changes in movement, ensuring the rotation point is identified with precision.

By combining OpenCV and MediaPipe, the software will:

• Track the rotation point for a wide range of pre-swing movements.
• Adjust to the unique movements of each golfer, providing personalized feedback.

Step 5: Generating Reports and Visual Feedback

Once the rotation point has been detected, the software can generate visual reports that highlight the pre-swing movements and display how the rotation point changes over time. This data can then be shared with coaches or stored for analysis.

You can also incorporate real-time feedback to display the golfer’s form during practice sessions. By using Python’s Matplotlib or similar visualization tools, the software can create simple, clear graphs that show the movement trends of the rotation point over time.

Conclusion

Developing a golf analysis software to track the rotation point of a golf club during pre-swing movements is a powerful tool for coaches and players. Using technologies like Python, OpenCV, and MediaPipe, this software can automatically capture, track, and analyze the club’s movement, providing insights into technique and performance.

By leveraging these technical solutions, the software can detect subtle movements, offer personalized feedback, and improve a golfer’s overall performance, helping them refine their game.