A Real-Time Deep Learning Approach to Badminton Player Positioning and Movement Tracking

Analyzing player positioning and movement in badminton matches is crucial for understanding match outcomes. Traditional post-match analysis often requires significant time and manpower, leading to inefficiencies. This study introduces an automated method for analyzing the movement and positioning of badminton players using recorded video footage. By leveraging player movement and positioning data, our approach aims to predict the outcomes of points during a match. The workflow consists of four key components. First, players are detected in the video using the YOLOv7 network model. Next, player coordinates are extracted frame by frame. These coordinates are then transformed into a standardized perspective using homography transformation. Finally, the data is visualized as heatmaps using a Gaussian distribution and fed into a 3D convolutional neural network (CNN) for model training. The goal is to evaluate how a player's movement and positioning during specific rallies influence wins or losses. Our approach focuses on individual players, offering deeper insights into their strategies and adaptations compared to their opponents. This method enhances players' awareness of their movement and positioning, providing valuable tools for improving performance.