MMSys'22 Grand Challenge on AI-based Video Production for Soccer
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 2 Feb 2022.
Builds on
9 of 31 references.
References · 9
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Built on by
Citing papers per year: 2024: 5; 2025: 0; 2026: 1.
6 papers.
- 2024SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimapcited by 34
- 2024X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Modelcited by 22
- 2024Towards AI-Powered Video Assistant Referee System (VARS) for Association Footballcited by 8
- 2024Investigating Event-Based Cameras for Video Frame Interpolation in Sportscited by 1
- 2026Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoningcited by 1
- 2024PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clipscited by 0
- 2026Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoning31 Mar 2026
- 2024PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips22 Jul 2024
- 2024Towards AI-Powered Video Assistant Referee System (VARS) for Association Football17 Jul 2024
- 2024Investigating Event-Based Cameras for Video Frame Interpolation in Sports2 Jul 2024
- 2024SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap17 Apr 2024
- 2024X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model7 Apr 2024