AuthorsJ. O. Valand, H. Kadragic, S. Hicks, V. Thambawita, C. Midoglu, T. Kupka, D. Johansen, M. Riegler and P. Halvorsen
TitleAI-Based Video Clipping of Soccer Events
AfilliationMachine Learning
Project(s)Department of Holistic Systems
StatusPublished
Publication TypeJournal Article
Year of Publication2021
JournalMachine Learning and Knowledge Extraction
Volume3
Issue4
Pagination990 - 1008
Date Published12/2021
PublisherMDPI
Abstract



The current gold standard for extracting highlight clips from soccer games is the use of manual annotations and clippings, where human operators define the start and end of an event and trim away the unwanted scenes. This is a tedious, time-consuming, and expensive task, to the extent of being rendered infeasible for use in lower league games. In this paper, we aim to automate the process of highlight generation using logo transition detection, scene boundary detection, and optional scene removal. We experiment with various approaches, using different neural network architectures on different datasets, and present two models that automatically find the appropriate time interval for extracting goal events. These models are evaluated both quantitatively and qualitatively, and the results show that we can detect logo and scene transitions with high accuracy and generate highlight clips that are highly acceptable for viewers. We conclude that there is considerable potential in automating the overall soccer video clipping process. 
 

URLhttps://www.mdpi.com/2504-4990/3/4/49/pdf
DOI10.3390/make3040049
Citation Key28313

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