AI-Enhanced Precision in Sport Taekwondo: Increasing Fairness, Speed, and Trust in Competition (FST.ai)
Keivan Shariatmadar, Ahmad Osman

TL;DR
This paper presents 'FST.ai', an AI-powered system that improves real-time decision-making in Sport Taekwondo by automating head kick detection, reducing decision time, and enhancing fairness and transparency, with potential applications across various sports.
Contribution
Introduction of 'FST.ai', a novel AI framework utilizing computer vision and deep learning for real-time officiating in Taekwondo and other sports, improving accuracy and speed.
Findings
Significantly reduced decision time from minutes to seconds.
Enhanced consistency and transparency in scoring.
Demonstrated sport-agnostic potential for various action detection tasks.
Abstract
The integration of Artificial Intelligence (AI) into sports officiating represents a paradigm shift in how decisions are made in competitive environments. Traditional manual systems, even when supported by Instant Video Replay (IVR), often suffer from latency, subjectivity, and inconsistent enforcement, undermining fairness and athlete trust. This paper introduces 'FST.ai' -- which is developed under the 'R3AL.ai' project, which serves as its Principal Investigator: r3al.ai -- a novel AI-powered framework designed to enhance officiating in Sport Taekwondo, particularly focusing on the complex task of real-time head kick detection and scoring. Leveraging computer vision, deep learning, and edge inference, the system automates the identification and classification of key actions, significantly reducing decision time from minutes to seconds while improving consistency and transparency.…
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Taxonomy
TopicsHuman Pose and Action Recognition · Video Analysis and Summarization · Sports Performance and Training
