New algorithms can predict the in-game actions of volleyball players with more than 80% accuracy. Now the Cornell Laboratory for Intelligent Systems and Controls, which developed the algorithms, is collaborating with the Big Red hockey team to expand the research project’s applications.
The algorithms are unique in that they take a holistic approach to action anticipation, combining visual data – for example, where an athlete is located on the court – with information that is more implicit, like an athlete’s specific role on the team.
“Computer vision can interpret visual information such as jersey color and a player’s position or body posture,” said Silvia Ferrari, who led the research. She is the John Brancaccio Professor of Mechanical and Aerospace Engineering. “We still use that real-time information, but integrate hidden variables such as team strategy and player roles, things we as humans are able to infer because we’re experts at that particular context.”
Ferrari and doctoral students Junyi Dong and Qingze Huo trained the algorithms to infer hidden variables by watching games – the same way humans gain their sports knowledge. The algorithms used machine learning to extract data from videos of volleyball games and then used that data to help make predictions when shown a new set of games.
The results were published in the journal ACM Transactions on Intelligent Systems and Technology on September 22, and show the algorithms can infer players’ roles – for example, distinguishing a defense-passer from a blocker – with an average accuracy of nearly 85%, and can predict multiple actions over a sequence of up to 44 frames with an average accuracy of more than 80%. The actions included spiking, setting, blocking, running, digging, squatting, standing, falling, and jumping.