Olympic AI 2020 Tokyo Olympics times athletes using AI.

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Omega Timing screenshots showing different sports data

Computer vision is keeping a close eye on athletes at the Summer Olympic Games in Tokyo.

What’s new: Omega Timing, a Swiss watchmaker and the Olympic Games’ official timekeeper, is providing systems that go far beyond measuring milliseconds. The company’s technology is tracking gameplay, analyzing players’ motions, and pinpointing key moments, Wired reported.

How it works: Omega Timing’s systems track a variety of Olympic sports including volleyball, swimming, and trampoline. Their output is intended primarily for coaches and athletes to review and improve performance, but it’s also available to officials and broadcasters.

  • The volleyball system classifies shots such as smashes, blocks, and spikes with 99 percent accuracy by tracking changes in the ball’s direction and velocity. It integrates gyroscopic sensors embedded in players’ clothing that monitor players’ movements. If the ball flies momentarily out of the camera’s sight, it computes the likely path. The company says the system is 99 percent accurate at determining different moves.
  • A pose estimator tracks gymnasts’ motions as they twist and flip on the trampoline. It also detects how precisely they land at the end of their routines.
  • An image recognition system watches water events, measuring the distance between swimmers, their speed, and the number of strokes each one takes.

Behind the news: Omega Timing has measured Olympic performance since 1932. It introduced photo-finish cameras at the 1948 Olympiad in London. Its systems are certified by the Swiss Federal Institute of Metrology.

Why it matters: Technology that helps athletes examine their performance in minute detail could give them a major edge in competition. It offers the rest of us a finer appreciation of their accomplishments.

We’re thinking: For this year’s games, the International Olympic Committee added to the schedule competitive skateboarding, surfing, and climbing. Next time, how about a data-centric AI competition?


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