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

• URL: http://arxiv.org/abs/2507.14657v2
• Date: Tue, 22 Jul 2025 14:19:12 GMT
• Title: AI-Enhanced Precision in Sport Taekwondo: Increasing Fairness, Speed, and Trust in Competition (FST.ai)
• Authors: Keivan Shariatmadar, Ahmad Osman,
• Abstract summary: 'FST.ai' is a novel AI-powered framework designed to enhance officiating in Sport Taekwondo.<n>It automates the identification and classification of key actions, significantly reducing decision time from minutes to seconds.<n>The framework 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. 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.

Related papers

• Fair Play in the Fast Lane: Integrating Sportsmanship into Autonomous Racing Systems [44.52724799426566]
  This paper introduces a bi-level game-theoretic framework to integrate sportsmanship (SPS) into versus racing.<n>At the high level, we model racing intentions using a Stackelberg game, where Monte Carlo Tree Search (MCTS) is employed to derive optimal strategies.<n>At the low level, vehicle interactions are formulated as a Generalized Nash Equilibrium Problem (GNEP), ensuring that all agents follow sportsmanship constraints while optimizing their trajectories.
  arXiv  Detail & Related papers  (2025-03-04T10:14:19Z)
• FACTS: Fine-Grained Action Classification for Tactical Sports [4.810476621219244]
  Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges.<n>We introduce FACTS, a novel approach for fine-grained action recognition that processes raw video data directly.<n>Our findings enhance training, performance analysis, and spectator engagement, setting a new benchmark for action classification in tactical sports.
  arXiv  Detail & Related papers  (2024-12-21T03:00:25Z)
• Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning [5.962453678471195]
  This work presents the experiments and solution outline for our teams winning submission in the Learn To Race Autonomous Racing Virtual Challenge 2022 hosted by AIcrowd. The objective of the Learn-to-Race competition is to push the boundary of autonomous technology, with a focus on achieving the safety benefits of autonomous driving. We focused our initial efforts on implementation of Soft Actor Critic (SAC) variants. Our goal was to learn non-trivial control of the race car exclusively from visual and geometric features, directly mapping pixels to control actions.
  arXiv  Detail & Related papers  (2024-10-24T06:16:52Z)
• Visual Agents as Fast and Slow Thinkers [88.1404921693082]
  We introduce FaST, which incorporates the Fast and Slow Thinking mechanism into visual agents.<n>FaST employs a switch adapter to dynamically select between System 1/2 modes.<n>It tackles uncertain and unseen objects by adjusting model confidence and integrating new contextual data.
  arXiv  Detail & Related papers  (2024-08-16T17:44:02Z)
• FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning [25.857375787748715]
  We present FightLadder, a real-time fighting game platform, to empower competitive MARL research. We provide implementations of state-of-the-art MARL algorithms for competitive games, as well as a set of evaluation metrics. We demonstrate the feasibility of this platform by training a general agent that consistently defeats 12 built-in characters in single-player mode.
  arXiv  Detail & Related papers  (2024-06-04T08:04:23Z)
• TacticAI: an AI assistant for football tactics [41.74699109772055]
  TacticAI is an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. We show that TacticAI's model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time.
  arXiv  Detail & Related papers  (2023-10-16T16:25:15Z)
• Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets [76.20705291443208]
  We view adversarial attacks as a bargaining game in which different players negotiate to reach an agreement on a joint direction of parameter updating. We design a novel framework that adjusts the budgets of different adversaries to avoid any player dominance. Experiments on standard benchmarks show that employing the proposed framework to the existing approaches significantly advances multi-target robustness.
  arXiv  Detail & Related papers  (2023-06-27T14:02:10Z)
• Towards Active Learning for Action Spotting in Association Football Videos [59.84375958757395]
  Analyzing football videos is challenging and requires identifying subtle and diverse-temporal patterns. Current algorithms face significant challenges when learning from limited annotated data. We propose an active learning framework that selects the most informative video samples to be annotated next.
  arXiv  Detail & Related papers  (2023-04-09T11:50:41Z)
• A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications [60.3327085463545]
  We present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, diving and badminton. We develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.
  arXiv  Detail & Related papers  (2022-06-02T13:19:36Z)
• Game Plan: What AI can do for Football, and What Football can do for AI [83.79507996785838]
  Predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. We illustrate that football analytics is a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI.
  arXiv  Detail & Related papers  (2020-11-18T10:26:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.