  Soccer has a considerable market share of the global sports industry, and the
interest in viewing videos from soccer games continues to grow. In this
respect, it is important to provide game summaries and highlights of the main
game events. However, annotating and producing events and summaries often
require expensive equipment and a lot of tedious, cumbersome, manual labor.
Therefore, automating the video production pipeline providing fast game
highlights at a much lower cost is seen as the "holy grail". In this context,
recent developments in Artificial Intelligence (AI) technology have shown great
potential. Still, state-of-the-art approaches are far from being adequate for
practical scenarios that have demanding real-time requirements, as well as
strict performance criteria (where at least the detection of official events
such as goals and cards must be 100% accurate). In addition, event detection
should be thoroughly enhanced by annotation and classification, proper
clipping, generating short descriptions, selecting appropriate thumbnails for
highlight clips, and finally, combining the event highlights into an overall
game summary, similar to what is commonly aired during sports news. Even though
the event tagging operation has by far received the most attention, an
end-to-end video production pipeline also includes various other operations
which serve the overall purpose of automated soccer analysis. This challenge
aims to assist the automation of such a production pipeline using AI. In
particular, we focus on the enhancement operations that take place after an
event has been detected, namely event clipping (Task 1), thumbnail selection
(Task 2), and game summarization (Task 3). Challenge website:
https://mmsys2022.ie/authors/grand-challenge.
