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Soccer has a considerable market share of the global sports industry, and theinterest in viewing videos from soccer games continues to grow. In thisrespect, it is important to provide game summaries and highlights of the maingame events. However, annotating and producing events and summaries oftenrequire expensive equipment and a lot of tedious, cumbersome, manual labor.Therefore, automating the video production pipeline providing fast gamehighlights at a much lower cost is seen as the "holy grail". In this context,recent developments in Artificial Intelligence (AI) technology have shown greatpotential. Still, state-of-the-art approaches are far from being adequate forpractical scenarios that have demanding real-time requirements, as well asstrict performance criteria (where at least the detection of official eventssuch as goals and cards must be 100% accurate). In addition, event detectionshould be thoroughly enhanced by annotation and classification, properclipping, generating short descriptions, selecting appropriate thumbnails forhighlight clips, and finally, combining the event highlights into an overallgame summary, similar to what is commonly aired during sports news. Even thoughthe event tagging operation has by far received the most attention, anend-to-end video production pipeline also includes various other operationswhich serve the overall purpose of automated soccer analysis. This challengeaims to assist the automation of such a production pipeline using AI. Inparticular, we focus on the enhancement operations that take place after anevent 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.

MMSys'22 Grand Challenge on AI-based Video Production for Soccer