In professional basketball, every foul can shift the game’s momentum. A recent study published in the International Journal of Computational Systems Engineering shows how machine vision – a form of AI that interprets visual data – is making foul detection clearer and more precise. By processing video frame-by-frame, this technology picks up on subtle movements and contact points that human referees might overlook.
The research compared matches featuring the Chinese national team with those of international competitors, revealing intriguing patterns. For example, Chinese players were more likely to commit fouls during shooting attempts, while their opponents fouled more during dribbling. Beyond just counting fouls, the system categorises different types—such as illegal hand use and player collisions—offering richer insights into game dynamics.
This detailed analysis can help coaches target specific weaknesses and provide players with precise feedback, while also supporting referees in refining their decision-making with data-driven insights. Although machine vision won’t replace human judgment, it stands as a valuable tool to help reduce errors and enhance overall game flow.
