Building an AI-Powered Tennis Tracking System

Feb 20, 2026 min read
Building an AI-Powered Tennis Tracking System

I recently completed a project I’m proud to share: tennis‑tracking on GitHub (AI/ML Tennis Match Analysis) — a system that uses computer vision and object tracking to analyze tennis match footage automatically.

Demo

Here’s a quick look at what the system produces (taken from the project README):

Tennis Tracking Demo

This demo shows how the system detects players and the ball, tracks their movement through each rally, and visualizes that information in a way that’s immediately insightful.

What It Does

This project takes raw tennis video footage and turns it into something far more useful for analysis:

  • Detects players and the tennis ball in every frame using YOLO-based models
  • Tracks objects across time so you can follow movement and activity
  • Identifies court lines to provide spatial context
  • Annotates videos with bounding boxes, IDs, and court overlays
  • Generates a simplified mini-court view with positions plotted in a top-down style

Whether you’re a coach, an analyst, or a tennis fan, this transforms a raw video into actionable visual data.

How It Works

The magic happens through a mix of modern computer vision and ML techniques:

Detection & Tracking

YOLO (You Only Look Once) models detect players and ball quickly and accurately.

A tracking system associates detections across frames to create continuous player/ball trajectories.

Court Line Detection

Using image processing techniques, the system estimates the position and geometry of court lines, anchoring player and ball positions in real space.

Output

The result is an annotated video where:

  • Players have consistent IDs and bounding boxes
  • The ball’s path is highlighted
  • Court lines contextualize everything spatially
  • A mini-court view summarizes the action (optional)

All of this is available once you run the pipeline on your input match video.

Tech Stack

This is built with:

  • Python
  • PyTorch & YOLO
  • OpenCV
  • Custom tracking and visualization logic

Quick Start

Just a few steps to get it running:

  1. Clone your project
git clone https://github.com/AdrianMelendez/tennis-tracking.git
cd tennis-tracking
  1. Set up Python environment
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
  1. Run on a match video
python main.py input_match.mp4 --output_dir output/

Your output videos with annotations will appear in the output/ directory when the process completes.

What I Learned

This project pushed me deeper into:

  • Real-world object detection challenges
  • Object tracking across video sequences
  • Spatial calibration of scenes (court lines, mini-map)
  • Balancing performance and accuracy in CV workflows

It’s a great example of how machine learning can make sense of unstructured video data.

Future Ideas

Possible upgrades:

  • Performance optimization for faster processing
  • Action classification (strokes, serve types)
  • Automated match statistics extraction

I want to thank Code in a Jiffy since this project was based on the ideas proposed in one of his videos.