mlsanityData to 14 Oct 2026

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

arXiv 2202.01031 ↗First posted 2 Feb 2022cs.CV cs.MMcited by 6

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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

  1. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  2. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,143
  3. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,068
  4. 2019BMN: Boundary-Matching Network for Temporal Action Proposal Generationcited by 329
  5. 2018BSN: Boundary Sensitive Network for Temporal Action Proposal Generationcited by 271
  6. 2019A Context-Aware Loss Function for Action Spotting in Soccer Videoscited by 56
  7. 2016To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videoscited by 34
  8. 2021TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domainscited by 16
  9. 2014Automatic Summarization of Soccer Highlights Using Audio-visual Descriptorscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 0; 2026: 1.

6 papers.