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Sender: ACM SIGMM Interest List <[log in to unmask]>
Date: Thu, 26 May 2022 15:54:24 +0200
Reply-To: Cise Midoglu <[log in to unmask]>
From: Cise Midoglu <[log in to unmask]>
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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:

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:
Please feel free to contact [log in to unmask] for questions and comments.

Goal! A practical guide to soccer video understandingAnthony 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

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.

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

For more information you can visit:

Cise Midoglu
Simula Research Laboratory



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