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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.14657 |
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| _version_ | 1866908461055344640 |
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| author | Shariatmadar, Keivan Osman, Ahmad |
| author_facet | Shariatmadar, Keivan Osman, Ahmad |
| contents | 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. Importantly, the methodology is not limited to Taekwondo. The underlying framework -- based on pose estimation, motion classification, and impact analysis -- can be adapted to a wide range of sports requiring action detection, such as judo, karate, fencing, or even team sports like football and basketball, where foul recognition or performance tracking is critical. By addressing one of Taekwondo's most challenging scenarios -- head kick scoring -- we demonstrate the robustness, scalability, and sport-agnostic potential of 'FST.ai' to transform officiating standards across multiple disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Enhanced Precision in Sport Taekwondo: Increasing Fairness, Speed, and Trust in Competition (FST.ai) Shariatmadar, Keivan Osman, Ahmad Computer Vision and Pattern Recognition Artificial Intelligence 68T45 I.2.10 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. Importantly, the methodology is not limited to Taekwondo. The underlying framework -- based on pose estimation, motion classification, and impact analysis -- can be adapted to a wide range of sports requiring action detection, such as judo, karate, fencing, or even team sports like football and basketball, where foul recognition or performance tracking is critical. By addressing one of Taekwondo's most challenging scenarios -- head kick scoring -- we demonstrate the robustness, scalability, and sport-agnostic potential of 'FST.ai' to transform officiating standards across multiple disciplines. |
| title | AI-Enhanced Precision in Sport Taekwondo: Increasing Fairness, Speed, and Trust in Competition (FST.ai) |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45 I.2.10 |
| url | https://arxiv.org/abs/2507.14657 |