Referees are aided by AI based SAOT to call offside in the 2022 FIFA World Cup

In this year's World Cup, FIFA is utilizing new artificial intelligence to assist referees in calling offside. Twelve cameras mounted to the stadium roof serve as the semi-automated offside technology (SAOT) system, which follows the ball and each player's movements. The official ball for the Qatar 2022 World Cup, Al Rihla, which means "the voyage" in Arabic, has a sensor attached that enables SAOT to compare the precise moment it was kicked with the position of the team's last defender and the other team's striker. SAOT tracks and recognizes players and the ball using artificial intelligence, calculating their whereabouts 50 times per second. The video match officials are informed whenever SAOT finds an offside. The referee, who has the final say, is informed. The system is described as "semi-automated" as a result. This level of accuracy is essential in close calls where referees struggle to swiftly signal offside. On this, a goal or even the outcome of the entire game, can occasionally depend.

The computer's hypothesis will be cross-checked with known things using a categorization method once there is enough information to make educated judgments. Normally, large video datasets of items that have already been recognized by humans — in this case, soccer players on the pitch — are used to teach artificial intelligence systems like SAOT. Artificial intelligence picks up on the players' appearance in this way. Following extensive training, this technology can detect and follow players with ease and speed.

Soccer matches frequently employ video assistant referee (VAR) technology. They take substantially longer than SAOT to detect an offside—about 70 seconds. Officials had to determine the ideal kick time and mark the offside line without the aid of VAR technology. They only need to check the system-recommended offside with SAOT. According to FIFA's website, the new procedure "happens within a few seconds and implies that offside rulings can be determined faster and more correctly." If the referee agrees with SAOT's recommendation, the system will produce a 3D cartoon of the offside broadcast to be shown to spectators on a sizable screen in the stadium.

To solve this issue, data scientists have created a variety of methods. Convolutional neural network is one of them (CNN). CNNs operate by layer-by-layer detecting things. When you press the item, you discover that it is soft in some places and hard in others. Your perception of the thing changes as a result of this movement: You now have enough knowledge to recognize that it has both soft and hard properties. The initial "layer" of detection would be represented by this information. This would be referred to as the "convolution" in CNNs. Following the initial layer's identification, you'll ask more questions about the object's texture, size, and shape. Each time one of these questions is addressed, a new layer arises, deepening your comprehension of the situation. This is a lot like how CNN operates.

According to FIFA, the SAOT system is currently "the most accurate offside support system accessible to video match officials" after three years of testing. The computer's hypothesis will be cross-checked with known things using a categorization method once there is enough information to make educated judgments. Normally, large video datasets of items that have already been recognized by humans — in this case, soccer players on the pitch — are used to teach artificial intelligence systems like SAOT. Artificial intelligence picks up on the players' appearance in this way. Following extensive training, this technology can detect and follow players with ease and speed.