Abstract
Abstract: Sports play a significant role in providing entertainment and recreational activities. As technology has advanced, video games featuring various sports and games have been developed using computer vision. In the game of golf, the golf swing is a crucial element that involves the entire body when players strike the ball. Having the correct posture is essential for a strong swing. However, beginners often struggle with identifying the keyframes they should focus on and which areas of their body they need to improve due to uneven timing and lack of expertise. To bridge this gap, this research proposes a neural network-based system for analyzing golf swings. The system utilizes monocular swing footage to offer an autonomous method for estimating the golfer's movement. Since amateur players often lack supervision during self-practice, this method can be particularly useful. The research also includes the design of an architecture that combines parts detection and parts association using image processing. By temporally aligning the swing videos, the system can estimate the golfer's pose, which can be further utilized for analysis.
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