Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 16, September 2022

A Tracing-based Tennis Coaching and Smart Training Platform
using Artificial Intelligence and Computer Vision


Feihong Liu1 and Yu Sun2, 1USA, 2California State Polytechnic University, USA


Athletes in technical sports often find it difficult to analyze their own technique while they’re playing [1]. Often, athletes look at the technique of professional players to identify problems they may have. Unfortunately, many types of techniques, such as forehand and backhand swings in tennis, are relatively similar between a beginner and a professional, making it more difficult for comparison. On the other hand, techniques that appear different between professionals and casual can also present different challenges. This is especially true for serves in tennis, where the speed of the swing, the motion of the player, and the angle of the camera recording the player all pose a challenge in analyzing differences between professional and learning tennis players [2]. In this paper, we used two machine learning approaches to compare the serves of two players. In addition, we also developed a website that utilizes these approaches to allow for convenient access and a better experience. We found that our algorithm is effective for comparing two serves of different speeds and synchronized the videos effectively.


Pose-estimation, Machine Learning, Scikit-learn.