ΑΙhub.org
 

Analysing ice hockey videos with deep learning

by
12 November 2021



share this:
Ice hockey match

Researchers at the University of Waterloo are developing technology to automatically analyse videos of hockey games using artificial intelligence. Their deep-learning technique can identify players by their sweater numbers with 90 percent accuracy.

“That is significant because the only major cue you have to identify a particular player in a hockey video is jersey number,” said Kanav Vats, a PhD student in systems design engineering who led the project. “Players on a team otherwise appear very similar because of their helmets and uniforms.”

Player identification is one aspect of a complicated challenge as members of the Vision and Image Processing (VIP) Lab at Waterloo work with an industry partner on AI software to analyse player performance and produce other data-driven insights.

The researchers built a data set of more than 54,000 images from National Hockey League games – the largest data set of its kind – and used it to train AI algorithms to recognize sweater numbers in new images.

Accuracy was boosted by representing the number 12, for instance, as both a two-digit number and two single digits, 1 and 2, put together, an approach known in the field of AI as multi-task learning.

“Using different representations to teach the same thing can improve performance,” Vats said. “We combined a holistic representation and a digit-wise representation with great results.”

The research team is also developing AI to track players in video, locate them on the ice and recognize what they are doing, such as taking a shot or checking an opposing player, for integration in a single system.

Detailed analytics have made great strides in hockey and other sports in recent years, but much of the work is still done by people watching broadcast video and taking notes.

“As you can imagine, a person manually annotating video of a full hockey game of three periods would take hours,” Vats said. “Machine-learning systems can produce data from videos in a matter of minutes.”

While they have focused so far on hockey, the researchers expect their technology could be transferred with modifications to other team sports, such as soccer.

Vats collaborated on the player identification work with his doctoral supervisors, Waterloo engineering professors David Clausi and John Zelek, and postdoctoral fellow Mehrnaz Fani.

He presented a paper, Multi-task learning for jersey number recognition in Ice Hockey, at the 4th International ACM Workshop on Multimedia Analysis in Sports in October 2021.



tags:


University of Waterloo




            AIhub is supported by:


Related posts :



#AAAI2024 workshops round-up 4: eXplainable AI approaches for deep reinforcement learning, and responsible language models

We hear from the organisers of two workshops at AAAI2024 and find out the key takeaways from their events.
12 April 2024, by

Deep learning-powered system maps corals in 3D

A system developed at EPFL can produce 3D maps of coral reefs from camera footage in just a few minutes.
11 April 2024, by

Is compute the binding constraint on AI research? Interview with Rebecca Gelles and Ronnie Kinoshita

We hear from authors of work presented at AAAI 2024 studying access to compute and the impact this has on AI research and researchers.
10 April 2024, by

Forthcoming machine learning and AI seminars: April 2024 edition

A list of free-to-attend AI-related seminars that are scheduled to take place between 9 April and 31 May 2024.
09 April 2024, by

Modeling extremely large images with xT

Introducing a new framework to model large images on contemporary GPUs while aggregating global context with local details.
08 April 2024, by

Going top shelf with AI to better track hockey data

Waterloo researchers get an assist from AI in identifying hockey players with greater accuracy and speed.
05 April 2024, by




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association