A Methodology for Detection and Estimation in the Analysis of the Golf Putting

This paper presents a methodology for visual detection and parameter estimation to analyze the effects of the variability in the performance of golf putting. A digital camera was used in each trial to track the putt gesture. The detection of the horizontal position of the golf club was performed using a computer vision technique, followed by an estimation algorithm divided in two different stages. On a first stage, diverse nonlinear estimation techniques were used and evaluated to extract a sinusoidal model of each trial. Secondly, several expert golf player trials were analyzed and, based on the results of the first stage, the Darwinian particle swarm optimization (DPSO) technique was employed to obtain a complete kinematical analysis and a characterization of each player’s putting technique. In this work, it is intended not only to test the performance of the DPSO method, but also to present a novel study in this field by identifying a putting “signature” of each player.