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Tech-driven officiating: Digital twins eliminate blown calls

Tech-driven officiating: Digital twins eliminate blown calls
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โš›๏ธRead original on Ars Technica

๐Ÿ’กSee how digital twins and computer vision are revolutionizing real-time physical tracking and decision-making.

โšก 30-Second TL;DR

What Changed

Utilization of 3D body scans to track player movement with high precision

Why It Matters

This integration sets a new standard for sports technology, demonstrating how computer vision and digital twin modeling can be applied to real-time physical environments. It paves the way for similar AI-driven precision tracking in other industries.

What To Do Next

Explore how your application can leverage sensor fusion and 3D modeling APIs to improve precision in real-time tracking tasks.

Who should care:Developers & AI Engineers

Key Points

  • โ€ขUtilization of 3D body scans to track player movement with high precision
  • โ€ขDigital twins enable multi-angle visualization for real-time referee decision-making
  • โ€ขIntegration of sensor data to eliminate human error in sports officiating

๐Ÿง  Deep Insight

Web-grounded analysis with 29 cited sources.

๐Ÿ”‘ Enhanced Key Takeaways

  • โ€ขThe 2026 World Cup will leverage a comprehensive end-to-end technology platform provided by Lenovo, integrating AI infrastructure, officiating systems, command centers, digital twins, and fan engagement tools.
  • โ€ขDigital twins of players are generated from rapid digital scans conducted before the tournament, creating highly detailed AI-powered avatars used for VAR reviews and fan visualizations.
  • โ€ขThe officiating system combines data from 16 stadium cameras, which track dozens of player data points multiple times per second, with an embedded Inertial Measurement Unit (IMU) sensor in the Adidas Trionda match ball, transmitting data 500 times per second for precise kick point detection.
  • โ€ขBeyond on-field officiating, digital twin technology is also being deployed to monitor crowd movement and optimize stadium operations, aiding in identifying congestion points and managing spectator flow.
  • โ€ขThe technology is classified as 'semi-automated' because while AI provides automated alerts and precise measurements, human video match officials retain the final authority to validate decisions, particularly for subjective interpretations like player interference.
๐Ÿ“Š Competitor Analysisโ–ธ Show
Feature/ProviderHawk-Eye (Sony)KINEXON Sports
Core OfferingsBall tracking, SkeleTRACK (skeletal tracking), SMART video review, Semi-Automated Offside Technology (SAOT), Goal-Line Technology (GLT), Electronic Line Calling (ELC)Real-time player & ball tracking, performance analytics, Semi-Automated Offside & touch detection, fan engagement solutions
Tracking TechnologiesOptical tracking (high-speed cameras), computer vision, AI models for pose estimation (29 skeletal points)Global Positioning Systems (GPS/GNSS), Local Positioning Systems (LPS) using Ultra-Wideband (UWB), Inertial Measurement Units (IMU), optical data tracking (computer vision)
Sports CoveredFootball (VAR, GLT, SAOT), Tennis, Cricket, Baseball, Basketball (NBA), American Football (NFL), Rugby, Volleyball, Badminton, Hurling, Gaelic FootballFootball, Basketball, Volleyball, Hockey, Handball, other outdoor team sports
FIFA Certification/PartnershipCore and critical officiating technology partner for FIFA events (e.g., SAOT for World Cup)FIFA Quality Programme certified for EPTS devices (GPS Elite, GPS Pro, LPS systems rated 'well above industry standard' for positional accuracy)
Latency/SpeedSAOT reduces VAR offside checks from ~70 seconds to 15-25 secondsReal-time data transmission with ultra-low latency
Data Metrics29 skeletal points per playerAround 120 live metrics (e.g., sprint diagnostics, workload, speed, acceleration)
HardwareUp to 12-16 dedicated tracking cameras (some 8K), IMU sensor in match ballWearable GPS/LPS/IMU sensors, network of stadium sensors for LPS

๐Ÿ› ๏ธ Technical Deep Dive

  • Camera Systems: The system utilizes 12 to 16 dedicated tracking cameras strategically mounted underneath the stadium roof. These cameras capture player movements at 50 frames per second.
  • Player Tracking: Advanced computer vision algorithms track up to 29 specific skeletal data points on each player's body, including all limbs and extremities relevant for offside decisions, in real-time.
  • Ball Tracking: The official match ball (e.g., Adidas Al Rihla, Trionda) contains an embedded Inertial Measurement Unit (IMU) sensor. This sensor transmits ball data 500 times per second, enabling highly precise detection of the exact kick point.
  • Digital Twin Creation: Players undergo rapid digital scans before the tournament to create highly detailed AI-powered avatars, which are then used to generate realistic 3D visual reconstructions of plays.
  • AI Processing: Artificial intelligence processes the combined limb-tracking and ball-tracking data in real-time to automatically detect potential offside situations and generate alerts for video match officials.
  • Decision Workflow: Upon an automated offside alert, video match officials manually review the proposed decision by checking the automatically selected kick point and the AI-generated offside line before communicating the decision to the on-field referee.
  • Fan Visualization: The same limb-tracking data and kick point information are used to create 3D animation replays, which are displayed on stadium screens and television broadcasts to explain decisions to fans.
  • Latency Improvement: This semi-automated offside technology is designed to reduce the average VAR offside check time from approximately 70 seconds to between 15 and 25 seconds.

๐Ÿ”ฎ Future ImplicationsAI analysis grounded in cited sources

AI will increasingly handle objective measurements in sports, allowing human officials to focus on subjective judgment calls.
The 'semi-automated' nature of current systems demonstrates a clear division of labor where AI excels at precise, data-driven measurements, while human expertise remains crucial for nuanced interpretations like interference with play.
Fan engagement will become significantly more immersive and personalized through AI-powered broadcasts and second-screen experiences.
The use of 3D player avatars, virtual recreations of plays, and AI-generated match visuals, along with AI-powered navigation tools for host cities, indicates a strong trend towards enriching the spectator experience both in-stadium and remotely.
Digital twin technology will expand beyond player officiating to encompass broader event and venue management, enhancing operational efficiency and safety.
Digital twins of stadiums are already being used for crowd and security management, simulating spectator movement and emergency scenarios to identify potential operational issues.

โณ Timeline

1999
Hawk-Eye technology developed by Paul Hawkins.
2001
Hawk-Eye debuted in cricket for broadcast purposes.
2003
Hawk-Eye introduced in tennis, evolving from a broadcast tool to an officiating aid.
2012
Goal-line technology, including Hawk-Eye, officially integrated into football rules by FIFA.
2018
Video Assistant Referee (VAR) system officially debuted in international football competitions, including the FIFA World Cup in Russia.
2022
Semi-automated offside technology (SAOT) with ball sensors and tracking cameras first used at the FIFA World Cup in Qatar.
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Original source: Ars Technica โ†—