{"id":707952,"date":"2026-06-18T10:21:29","date_gmt":"2026-06-18T14:21:29","guid":{"rendered":"https:\/\/www.rochester.edu\/newscenter\/?p=707952"},"modified":"2026-06-18T10:28:52","modified_gmt":"2026-06-18T14:28:52","slug":"what-is-computer-vision-examples-soccer-technology-707952","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/what-is-computer-vision-examples-soccer-technology-707952\/","title":{"rendered":"How AI helps World Cup referees make the call"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"<p>Computer vision won\u2019t replace referees at the World Cup. But it can help them make better calls when every inch matters.<\/p>\n","protected":false},"author":1242,"featured_media":708002,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[116],"tags":[24292,43022,18802,18632],"class_list":["post-707952","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sci-tech","tag-artificial-intelligence","tag-chenliang-xu","tag-department-of-computer-science","tag-hajim-school-of-engineering-and-applied-sciences"],"acf":{"external":"","hide_featured_image":true,"related":"show_related","style":[706792,639342,486392],"content_modules":[{"acf_fc_layout":"media_text_new","anchor":"","class":"pt-0","overline":"","title":"","content":"","cta":"","bg":"#ffffff","type":"video","image":"","embed":"","video":708152,"layout":"above","animation":"animation-false"},{"acf_fc_layout":"text","anchor":"","css_class":"pt-0","title":"<h2>Computer vision won\u2019t replace referees at the World Cup. But it can help them make better calls when every inch matters.<\/h2>","content":"More than 1.5 billion people worldwide are <a href=\"https:\/\/sports.economictimes.indiatimes.com\/news\/media-broadcasting\/world-cup-final-viewership-expected-to-exceed-1-5-billion\/130385750\">expected to watch<\/a> the 2026 World Cup finals. With that many fans scrutinizing every pass, touch, and goal, FIFA is leaning on advanced computer vision technology to help referees make faster, more accurate calls on the way to crowning this year\u2019s victors.\r\n\r\nThis year, the tournament\u2019s officiating toolkit includes <a href=\"https:\/\/www.sportsbusinessjournal.com\/Articles\/2026\/03\/30\/power-players-fifa-world-cup-2026-hawk-eye-innovations\/\">Sony\u2019s Hawk-Eye technology<\/a>, which supports video assistant referees (VAR), goal-line technology, advanced semi-automated offside technology, and a \u201clast touch\u201d feature for corner and goal kicks.\r\n\r\n\u201cIt\u2019s a very sophisticated system that glues together multiple computer vision techniques,\u201d says <a href=\"https:\/\/www.cs.rochester.edu\/people\/faculty\/xu_chenliang\/index.html\">Chenliang Xu<\/a>, an associate professor of computer science at the <a href=\"https:\/\/www.rochester.edu\/\">University of Rochester<\/a> and an expert in computer vision. \u201cYou have calibrated cameras, real-time vision models to detect the ball, players, and their poses, as well as a decision layer to identify when some sort of intervention needs to happen.\u201d\r\n\r\nFor players and fans alike, the result may be shorter waits for close calls.\r\n\r\nFIFA first deployed Sony\u2019s Hawk-Eye ball-tracking technology in 2012 at the Club World Cup. At the 2022 World Cup, FIFA <a href=\"https:\/\/inside.fifa.com\/innovation\/media-releases\/semi-automated-offside-technology-to-be-used-at-fifa-world-cup-2022-tm\">introduced semi-automated offside technology<\/a>, which combines limb- and ball-tracking data with artificial intelligence to provide referees and video match officials with information in mere seconds to inform offside decisions.","background_color":"#ffffff","width":"width-medium"},{"acf_fc_layout":"media_text_new","anchor":"","class":"pt-0","overline":"","title":"<h3>Before VAR, it was \u2018the hand of god\u2019<\/h3>","content":"In the 1986 World Cup quarterfinals, Argentina\u2019s Diego Maradona scored one of soccer\u2019s most infamous goals\u2014using his hand to punch the ball into the net. The referee never saw the infraction, and the goal stood. Maradona later described it as \u201ca little with the head of Maradona and a little with the hand of God.\u201d\r\n\r\nToday, a combination of high-speed cameras, computer vision, and video review would almost certainly flag the violation within seconds. It\u2019s a reminder of how far officiating technology has come\u2014and why FIFA continues to invest in tools designed to help officials get the biggest calls right.","cta":"","bg":"#ffc70a","type":"embed","image":"","embed":"https:\/\/www.youtube.com\/watch?v=-ccNkksrfls","video":"","layout":"right","animation":"animation-false"},{"acf_fc_layout":"text","anchor":"","css_class":"pt-15","title":"","content":"<h3><strong>How does computer vision track players and the ball? <\/strong><\/h3>\r\nPlayer- and ball-tracking systems rely on dedicated computer vision neural networks trained on millions of annotated images and videos.\r\n\r\n\u201cTraining a computer-vision algorithm to detect a human pose is like teaching a child how to recognize things\u2014you feed it different examples,\u201d says Xu. By taking in a massive collection of examples, the deep neural networks learn to locate players, their body parts, and the ball during a match. Beyond recognizing players and the ball in individual frames, these systems continuously track them over time and across multiple camera views, which is critical for determining offside positions and identifying who touched the ball last.\r\n\r\nDuring this year\u2019s World Cup matches, <a href=\"https:\/\/inside.fifa.com\/innovation\/news\/offside-decisions-referee-body-cams-innovation-world-cup-2026\">sixteen optical tracking cameras are positioned around each stadium<\/a>\u2014feeding those tracking systems with live data during games.\r\n<blockquote>\u201cTraining a computer-vision algorithm to detect a human pose is like teaching a child how to recognize things\u2014you feed it different examples.\u201d<\/blockquote>\r\nWhy so many cameras? A single camera view can be blocked or misleading. Multiple cameras enable the triangulation of the ball, players, and boundaries to create precise reconstructions in three dimensions. Those 3D reconstructions are generated in seconds and then provided to officials who make the final call.\r\n\r\n\u201cJust like with humans, if you block one of your eyes, it\u2019s very hard to perceive depth,\u201d says Xu. \u201cBut when you have both of your eyes open, you can actually fill out the depth and 3D location of the object you\u2019re looking at.\u201d\r\n<h3><strong>How can AI refereeing tools work so quickly?<\/strong><\/h3>\r\nFIFA estimates that the tracking cameras provide more than 150 million tracking data points per match. That\u2019s a lot of data to manage. So, the speed comes from specialization.\r\n\r\n\u201cWhen FIFA deploys these deep neural networks, they only need them to work well in very particular scenarios,\u201d says Xu. \u201cYou don\u2019t necessarily need your algorithm to recognize a bird, fans, or anything else unrelated to the match; you just need them to recognize the players.\u201d\r\n\r\nThat narrower focus helps the system process a still massive stream of match data quickly. A model may begin as a large neural network trained on many kinds of images, according to Xu. Then, it gets refined and scaled back for the specific problems it needs to solve on the pitch.\r\n\r\n[caption id=\"attachment_708222\" align=\"aligncenter\" width=\"2000\"]<img class=\"size-full wp-image-708222\" src=\"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2026\/06\/fea-what-is-computer-vision-examples-soccer-technology-video-assistant-referee-GettyImages-2240828136.jpg\" alt=\"Referee Daniel Siebert checks the pitchside VAR screen.\" width=\"2000\" height=\"1200\" \/> <strong>PLAY IT AGAIN, CAM:<\/strong> A referee checks the pitchside virtual assistant referee (VAR) screen during the Group J FIFA World Cup 2026 qualifier match. (Getty Images)[\/caption]\r\n\r\nXu says these applications would have been hard to imagine just a decade or so ago. Two advances made the systems of today possible: deep neural networks and graphics processing units (GPUs).\r\n\r\nThe deep neural networks\u2014machine learning systems inspired by the human brain\u2014that have emerged in recent years dramatically improved performance on visual recognition and tracking tasks compared with many earlier approaches. These networks excel at taking vast amounts of unstructured data and identifying complex relationships with little human intervention.\r\n\r\n\u201cNeural networks have changed the whole paradigm since it\u2019s no longer necessary to have manually designed features that we need to train the system to look for,\u201d says Xu. \u201cYou input the image and the system automatically learns the visual representations needed for the task.\u201d\r\n\r\nMeanwhile, the capabilities of GPUs\u2014the electronic circuits specifically designed to process and generate videos, images, and 3D graphics\u2014jumped significantly in the 2010s, making today\u2019s large-scale AI systems possible.\r\n\r\n\u201cThe computing power has gotten so much better, so we can train those large neural networks with tons of data that we couldn\u2019t imagine maybe 10 or 15 years ago,\u201d says Xu.\r\n<h3><strong>Where else is this technology used?<\/strong><\/h3>\r\nWhile similar systems are used for <a href=\"https:\/\/www.espn.com\/nfl\/story\/_\/id\/44493002\/nfl-implementing-hawk-eye-system-measure-first-downs\">measuring first downs in NFL games<\/a>, <a href=\"https:\/\/www.cnbc.com\/2023\/09\/09\/how-sonys-hawk-eye-works-at-the-us-open.html\">line-calling at the US Open<\/a>, and <a href=\"https:\/\/pr.nba.com\/nba-sony-hawk-eye-innovations-partnership\/\">making goaltending calls in the NBA<\/a>, Xu says the technology has applications outside of sports as well.\r\n\r\n\u201cThis is very similar to the technology that you deploy in self-driving cars,\u201d says Xu. \u201cThose systems need to figure out the vehicle\u2019s environment, detect different traffic participants and track them over time, and have a decision system built inside to choose whether to accelerate, apply the brakes, or change lanes.\u201d\r\n\r\nXu thinks the underlying computer vision technology could be used for security, surveillance, and other settings where cameras need to follow activity across a complex physical space.\r\n\r\n\u201cIf you want a smart system that tracks people\u2019s activity on a property that contains multiple buildings\u2014indoors and outdoors\u2014and you have cameras deployed in different locations throughout the property, you can see the parallels,\u201d says Xu. \u201cJust like in a soccer match, you could use these systems for person detection and tracking and perhaps reviewing a 3D reconstruction of the property.\u201d\r\n\r\nEven as the technology behind the World Cup becomes faster and more sophisticated, Xu says the human element remains at the heart of the game. Computer vision can help officials determine whether a player\u2019s toe drifted offside or who touched the ball last. But at least for now, it can\u2019t predict the brilliance of a last-minute goal, the agony of a missed penalty kick, or the collective joy and heartbreak that keep billions of fans watching until the final whistle.","background_color":"#ffffff","width":"width-medium"}]},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How AI helps World Cup referees make the call<\/title>\n<meta name=\"description\" content=\"Computer vision won\u2019t replace referees at the World Cup. 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