A Multi-stage deep architecture for summary generation of soccer videos
- URL: http://arxiv.org/abs/2205.00694v1
- Date: Mon, 2 May 2022 07:26:35 GMT
- Title: A Multi-stage deep architecture for summary generation of soccer videos
- Authors: Melissa Sanabria, Fr\'ed\'eric Precioso, Pierre-Alexandre Mattei, and
Thomas Menguy
- Abstract summary: We propose a method to generate the summary of a soccer match exploiting both the audio and the event metadata.
The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries.
- Score: 11.41978608521222
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Video content is present in an ever-increasing number of fields, both
scientific and commercial. Sports, particularly soccer, is one of the
industries that has invested the most in the field of video analytics, due to
the massive popularity of the game and the emergence of new markets. Previous
state-of-the-art methods on soccer matches video summarization rely on
handcrafted heuristics to generate summaries which are poorly generalizable,
but these works have yet proven that multiple modalities help detect the best
actions of the game. On the other hand, machine learning models with higher
generalization potential have entered the field of summarization of
general-purpose videos, offering several deep learning approaches. However,
most of them exploit content specificities that are not appropriate for sport
whole-match videos. Although video content has been for many years the main
source for automatizing knowledge extraction in soccer, the data that records
all the events happening on the field has become lately very important in
sports analytics, since this event data provides richer context information and
requires less processing. We propose a method to generate the summary of a
soccer match exploiting both the audio and the event metadata. The results show
that our method can detect the actions of the match, identify which of these
actions should belong to the summary and then propose multiple candidate
summaries which are similar enough but with relevant variability to provide
different options to the final editor. Furthermore, we show the generalization
capability of our work since it can transfer knowledge between datasets from
different broadcasting companies, different competitions, acquired in different
conditions, and corresponding to summaries of different lengths
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