Markerless human motion tracking for golf swing application

Abstract

Sports video tracking is one of the emerging topics that have grabbed a lot of attention due to its high commercial potential. A variety of sports, including tennis, soccer, gymnastics, running, and golf have been utilized to demonstrate new ideas in sports motion tracking. The main challenge for this research work concerns the extraction of a highly complex articulated motion of a golf player performing sports action from a video scene. This research work focuses on developing a markerless human motion tracking system that tracks major body parts of an athlete directly from a sports broadcast video. A hybrid tracking method is proposed in this research work which consists of a combination of three algorithms namely the pyramidal Lucas-Kanade optical flow, normalized correlation based template matching and background subtraction. These algorithms are used to track the head, body, hands, shoulders, knees and the feet of a golfer while the individual is performing a full swing. Finally, the output results are tracked and mapped onto a 2D articulated human stick model to represent the pose of the golfer. The research work has been tested on a broadcast video of a golfer on various background complexities.