Applying Pose Estimation to Predict Amateur Golf Swing Performance Using Edge Processing

We present an analysis and approach for utilizing vision-based pose estimation to find key video frames in a full golf swing to assist in providing feedback for improvement. Using both still photos and videos, the proposed system discovers key moments in the golf swing to be evaluated and can identify metrics of the golfer such as posture, swing tempo, and swing consistency. These key frames can also predict the swing outcome by creating a path projection. The images and videos processed analyze the golf swing from a down the line perspective. For the computations, we utilize a low cost tensor processing unit (TPU) to run inference and data processing which set the performance baseline for the video capturing system. Hardware and pose estimation limitations and inaccuracies are identified and compensated for by using a Savitzky-Golay filter. This will allow for a markerless swing tracking analysis system in a low cost, small form factor.