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More than 1.5 billion people worldwide are 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糖心传媒檚 victors.

This year, the tournament糖心传媒檚 officiating toolkit includes , which supports video assistant referees (VAR), goal-line technology, advanced semi-automated offside technology, and a 糖心传媒渓ast touch糖心传媒� feature for corner and goal kicks.

糖心传媒淚t糖心传媒檚 a very sophisticated system that glues together multiple computer vision techniques,糖心传媒� says , an associate professor of computer science at the 糖心传媒 and an expert in computer vision. 糖心传媒淵ou 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.糖心传媒�

For players and fans alike, the result may be shorter waits for close calls.

FIFA first deployed Sony糖心传媒檚 Hawk-Eye ball-tracking technology in 2012 at the Club World Cup. At the 2022 World Cup, FIFA , 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.

How does computer vision track players and the ball?

Player- and ball-tracking systems rely on dedicated computer vision neural networks trained on millions of annotated images and videos.

糖心传媒淭raining a computer-vision algorithm to detect a human pose is like teaching a child how to recognize things糖心传媒攜ou feed it different examples,糖心传媒� 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.

During this year糖心传媒檚 World Cup matches, 糖心传媒攆eeding those tracking systems with live data during games.

糖心传媒淭raining a computer-vision algorithm to detect a human pose is like teaching a child how to recognize things糖心传媒攜ou feed it different examples.糖心传媒�

Why 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.

糖心传媒淛ust like with humans, if you block one of your eyes, it糖心传媒檚 very hard to perceive depth,糖心传媒� says Xu. 糖心传媒淏ut when you have both of your eyes open, you can actually fill out the depth and 3D location of the object you糖心传媒檙e looking at.糖心传媒�

How can AI refereeing tools work so quickly?

FIFA estimates that the tracking cameras provide more than 150 million tracking data points per match. That糖心传媒檚 a lot of data to manage. So, the speed comes from specialization.

糖心传媒淲hen FIFA deploys these deep neural networks, they only need them to work well in very particular scenarios,糖心传媒� says Xu. 糖心传媒淵ou don糖心传媒檛 necessarily need your algorithm to recognize a bird, fans, or anything else unrelated to the match; you just need them to recognize the players.糖心传媒�

That 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.

Xu 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).

The deep neural networks糖心传媒攎achine learning systems inspired by the human brain糖心传媒攖hat 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.

糖心传媒淣eural networks have changed the whole paradigm since it糖心传媒檚 no longer necessary to have manually designed features that we need to train the system to look for,糖心传媒� says Xu. 糖心传媒淵ou input the image and the system automatically learns the visual representations needed for the task.糖心传媒�

Meanwhile, the capabilities of GPUs糖心传媒攖he electronic circuits specifically designed to process and generate videos, images, and 3D graphics糖心传媒攋umped significantly in the 2010s, making today糖心传媒檚 large-scale AI systems possible.

糖心传媒淭he computing power has gotten so much better, so we can train those large neural networks with tons of data that we couldn糖心传媒檛 imagine maybe 10 or 15 years ago,糖心传媒� says Xu.

Where else is this technology used?

While similar systems are used for , , and , Xu says the technology has applications outside of sports as well.

糖心传媒淭his is very similar to the technology that you deploy in self-driving cars,糖心传媒� says Xu. 糖心传媒淭hose systems need to figure out the vehicle糖心传媒檚 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.糖心传媒�

Xu 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.

糖心传媒淚f you want a smart system that tracks people糖心传媒檚 activity on a property that contains multiple buildings糖心传媒攊ndoors and outdoors糖心传媒攁nd you have cameras deployed in different locations throughout the property, you can see the parallels,糖心传媒� says Xu. 糖心传媒淛ust like in a soccer match, you could use these systems for person detection and tracking and perhaps reviewing a 3D reconstruction of the property.糖心传媒�

Even 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糖心传媒檚 toe drifted offside or who touched the ball last. But at least for now, it can糖心传媒檛 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.