Real-Time Pose Recognition for Billiard Players Using Deep Learning

Abstract

In this book chapter, the authors propose a method for player pose recognition in billiards matches by combining keypoint extraction and an optimized transformer. Given that those human pose analysis methods usually require high labour costs, the authors explore deep learning methods to achieve real-time, high-precision pose recognition. Firstly, they utilize human key point detection technology to extract the key points of players from real-time videos and generate key points. Then, the key point data is input into the transformer model for pose analysis and recognition. In addition, the authors design a human skeletal alignment method for comparison with standard poses. The experimental results show that the method performs well in recognizing players' poses in billiards matches and provides real-time and timely feedback on players' pose information. This research project provides a new and efficient tool for training billiard players and opens up new possibilities for applying deep learning in sports analytics. In addition, one of these contributions is the creation of a dataset for pose recognition.