In many team sports, in-depth analytics enable teams to have the edge in strategy, scouting, and performance benchmarking. Ensuring Racing Safety. Making Smarter Scouting Decisions. Enhancing Shooting Accuracy. Improving Strategy and Viewing Experience.
On the one hand, sports are an engaging type of entertainment that connects different cultures around the world. On the other hand, many types of sports are often complex multidimensional systems that incorporate a plethora of data points that make one team or athlete better than the other. AI-enabled technologies like computer vision are poised to have an impact on all parts of this equation.
Let’s see how the use of computer vision software in sports is changing fans’ viewing experience and impacts the ways athletes analyze their performance.
In many team sports, in-depth analytics enable teams to have the edge in strategy, scouting, and performance benchmarking. Conventional game statistics that provide dry summaries of goals, assists, and shots are not sufficient to understand the performance on a granular level. With AI and computer vision, we can now understand the root cause of a game outcome, be it a specific player’s condition or move.
AI researchers at MIT and UCLA discuss the need to address the "dark matter" of computer vision, the things that aren't visible in pixels. This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. What makes us humans so good at making sense of visual data? That’s a question that has preoccupied artificial intelligence and computer vision scientists for decades.
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