Design and implementation of a low-cost line judge system for badminton using mobile phones

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Journal ISSN

Volume Title

School of Science | Master's thesis

Authors

Date

2025-12-30

Department

Major/Subject

Security and Cloud Computing

Mcode

Degree programme

Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences

Language

en

Pages

42

Series

Abstract

Line calling is a well-known problem in the badminton community, affecting players from amateur to professional levels. It requires high precision due to the fast speeds of the shuttlecock. While professional systems such as Hawk-Eye can assist referees and improve fairness, they are too expensive for minor tournaments. This thesis investigates the feasibility of a low-cost line-judging system using players’ own mobile phones. The proposed system combines a cross-platform mobile application with a dedicated backend server to perform heavy processing tasks. A user-involved method for court calibration is proposed to improve robustness across different court environments. Evaluation on both broadcast and manually recorded videos shows that the system achieves 82\% accuracy in determining in/out decisions when tracking is successful, with an overall pipeline success rate of 70\%. The study identifies 720p resolution and 60 fps as the minimum requirements for reliable performance. While the results demonstrate the potential of mobile-based automation for grassroots badminton, practical deployment is constrained by processing delays, sensitivity to complex backgrounds, and the need for optimal camera placement. These findings provide a foundation for future improvements aimed at enhancing robustness, reducing latency, and enabling wider adoption in recreational and local-level badminton.

Description

Supervisor

Siekkinen, Matti

Keywords

sports technology, video analysis, badminton, mobile application, object detection, deep learning

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202601221833

Collections

[dipl] Perustieteiden korkeakoulu / SCI