See Every Rally: Computer Vision Based Recreational Tennis Analytics Tool

Publication Date

2026

Presentation Length

Poster/Gallery presentation

College

College of Sciences & Mathematics

Department

Math and Computer Science, Department of

Student Level

Undergraduate

Faculty Mentor

Christina Davis

Presentation Type

Poster

Summary

Detailed performance analytics in tennis have long been reserved for professional players with access to expensive tracking systems. Today, applications do exist for recreational players, but often have high costs and limitations. This project builds a tool that takes regular smartphone or camera footage of a tennis match and automatically tracks where players move, how they cover the court, and where the ball goes throughout the match. Using computer vision and AI-based detection models (DINOv2 and YOLO), the system processes raw video and pulls out meaningful performance data without requiring specialized equipment. That data is then displayed in a simple, visual dashboard that gives players easy-to-understand insights like court coverage maps, movement patterns, and rally trends. The goal is to give recreational tennis players access to the same kind of performance feedback that professionals rely on, without the costs.

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