Deep learning for action spotting in association football videos
Clicks: 16
ID: 282439
2024
The task of action spotting consists in both identifying actions and
precisely localizing them in time with a single timestamp in long, untrimmed
video streams. Automatically extracting those actions is crucial for many
sports applications, including sports analytics to produce extended statistics
on game actions, coaching to provide support to video analysts, or fan
engagement to automatically overlay content in the broadcast when specific
actions occur. However, before 2018, no large-scale datasets for action
spotting in sports were publicly available, which impeded benchmarking action
spotting methods. In response, our team built the largest dataset and the most
comprehensive benchmarks for sports video understanding, under the umbrella of
SoccerNet. Particularly, our dataset contains a subset specifically dedicated
to action spotting, called SoccerNet Action Spotting, containing more than 550
complete broadcast games annotated with almost all types of actions that can
occur in a football game. This dataset is tailored to develop methods for
automatic spotting of actions of interest, including deep learning approaches,
by providing a large amount of manually annotated actions. To engage with the
scientific community, the SoccerNet initiative organizes yearly challenges,
during which participants from all around the world compete to achieve
state-of-the-art performances. Thanks to our dataset and challenges, more than
60 methods were developed or published over the past five years, improving on
the first baselines and making action spotting a viable option for the sports
industry. This paper traces the history of action spotting in sports, from the
creation of the task back in 2018, to the role it plays today in research and
the sports industry.
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Authors | Silvio Giancola; Anthony Cioppa; Bernard Ghanem; Marc Van Droogenbroeck |
Journal | arXiv |
Year | 2024 |
DOI | DOI not found |
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