Waterloo Engineering researchers have upped the evolving game of automated hockey analysis with new advances to follow the puck and track player movement with more accuracy than ever before.
In two recent studies, the researchers developed artificial intelligence (AI) software to overcome challenges posed by motion blurring and obstructed views in broadcast video of the fast-paced sport.
“These improvements in detection accuracy could transform how coaches, teams, and broadcasters analyze game dynamics, leading to better strategic decisions and more engaging fan experiences,” said Dr. David Clausi, a professor of systems design engineering.
In one study, researchers built a model that leverages the fact players usually keep their eyes on the puck during games to help infer where it is based on body position and the direction of their gaze.
“Finding the puck in broadcast video is one of the toughest problems in sports vision, so seeing our system accurately predict its location using contextual cues was incredibly rewarding,” said Liam Salass, a master’s student who was lead author of the study. “It was like we'd given computers real game sense.”
The second study involved development of an AI-based framework that improves how multiple moving players are tracked in sports videos.
“Tracking a hockey player on a breakaway is relatively easy,” said Dr. John Zelek, also a systems design engineering professor and a director with Clausi of the Vision and Image Processing (VIP) Lab.
“It’s much more difficult to track and differentiate players in a scrum along the boards or in front of the net. (Our system) can tackle these difficult situations and tell us, for example, who deflected the puck and scored.”
Go to How AI can track hockey action from faceoff to finish for the full story.