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 :



New AI tool helps match enzymes to substrates

  24 Oct 2025
A new machine learning-powered tool can help researchers determine how well an enzyme fits with a desired target.

#AIES2025 social media round-up

  24 Oct 2025
Find out what participants got up to at the Conference on Artificial Intelligence, Ethics, and Society.

Looking ahead to #ECAI2025

  23 Oct 2025
Find out what the programme has in store at the European Conference on AI.

Congratulations to the #AIES2025 best paper award winners!

  21 Oct 2025
The four winners of best paper prizes were announced during the opening ceremony at AIES.

From the telegraph to AI, our communications systems have always had hidden environmental costs

  20 Oct 2025
Drawing parallels between new technologies of the past and today.

What’s on the programme at #AIES2025?

  17 Oct 2025
The conference on AI, ethics, and society will take place in Madrid from 20-22 October.

Generative AI model maps how a new antibiotic targets gut bacteria

  16 Oct 2025
Researchers used a GenAI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria.

What’s coming up at #IROS2025?

  15 Oct 2025
Find out what the International Conference on Intelligent Robots and Systems has in store.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence