Baseball Action Classification Based on OpenPose

Abstract

This research presents an innovative approach to sports analytics through the application of Convolutional Neural Networks (CNNs) and OpenPose, a real-time pose estimation technology, for the classification of baseball actions. Focused on the accurate categorization of key baseball movements—Pitching, Batting, Fielding, Throwing, Base Running, Defensive Positioning, and Catching—the study developed a CNN model tailored to analyze skeletal data derived from OpenPose. This model was trained and tested on a diverse dataset collected from various baseball games and training sessions, ensuring a comprehensive and realistic evaluation. Impressively, the model achieved a 90% accuracy rate in classifying the aforementioned baseball actions, as validated by a detailed confusion matrix analysis. This high level of precision demonstrates the significant potential of combining advanced pose estimation with machine learning in sports. The results not only offer new insights into athlete performance enhancement and injury prevention but also mark a substantial advancement in the application of technological tools in sports analytics. This research provides valuable implications for coaches, athletes, and sports scientists, highlighting a novel avenue for enhanced athletic analysis and training.