Dear colleagues,
the 2022 Symposium of the Norwegian AI Society (NAIS 2022) will be held between May 31 - June 1 at the Oslo Metropolitan University (OsloMet). There will be 3 co-located tutorials which will take place before and after the main conference program: https://aisociety.no/nais2022/#cft

You can find below a call for participation in the tutorial "Goal! A practical guide to soccer video understanding" by the SoccerNet team. 
We welcome both physical and virtual participation. You can register using the following link to receive more detailed participation information.
Free registration: https://www.eventbrite.com/e/nais-2022-tutorials-tickets-336399539077
Please feel free to contact [log in to unmask] for questions and comments.

Goal! A practical guide to soccer video understanding

Anthony Cioppa (ULiège), Silvio Giancola (KAUST), Adrien Deliège (ULiège), Floriane Magera (EVS Broadcast Equipment and ULiège), Vladimir Somers (UCLouvain, EPFL, and SportRadar), Le Kang (Baidu Research), Xin Zhou (Baidu Research), Bernard Ghanem (KAUST), and Marc Van Droogenbroeck (ULiège)

Date: Wednesday, June 1 @ 13:00-16:00

The SoccerNet dataset released in 2018 marked the start of large-scale soccer analysis in academia, gathering a growing research community which now expands to the industry. Broadcast soccer video understanding is an attractive topic for graduate students with many potential applications, like highlights composition and statistics generation. Besides, it encompasses natural yet challenging tasks for computer vision professionals, such as action spotting, camera calibration, player re-identification and tracking. It also comes with specific difficulties to handle fast-paced actions, players of similar appearance and replays through various camera views. All these aspects make soccer a rich yet often overlooked playground for research.

This tutorial focuses on the practical side of building soccer video understanding pipelines: which data is available, how to annotate it, how to use it, which useful tasks can be defined, tackled, and assessed, and which challenges keep the community and industries busy. Demos with Python code will be presented step-by-step to cover a large panel of soccer-related tasks. The instructors and presenters of the tutorial are experienced scientists from academia and industry that lead the soccer research community and develop cutting-edge technologies for sports broadcasts.

This tutorial is tailored for computer vision master students and their professors seeking computer vision classes or thesis projects, for PhD candidates focusing on spatio-temporal aspects of video analysis, for researchers and industrials willing to apply AI techniques within sports broadcasts, and for any soccer enthusiast. The download information of the SoccerNet dataset indicates that all those types of profiles regularly use the dataset. The tutorial assumes basic knowledge of Python and neural networks. Upon completion of the tutorial, attendees will have at hand various pipelines to tackle tasks such as action spotting, player tracking, player re-identification, camera calibration, that they can use not only in soccer-related projects but also transfer to their own research. All the material produced within the tutorial will be made available online.

TimeTopic
13:00Introduction
13:10Soccer fundamentals
13:35Introducing massive annotations for soccer
14:00Camera calibration
14:25Multi-view re-identification
14:50Player tracking
15:15Action spotting and replay grounding
15:40Challenges, Q&A and future directions

For more information you can visit: https://aisociety.no/nais2022/#cft

--
Cise Midoglu
Simula Research Laboratory



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