@article {28313,
	title = {AI-Based Video Clipping of Soccer Events},
	journal = {Machine Learning and Knowledge Extraction},
	volume = {3},
	year = {2021},
	month = {12/2021},
	pages = {990 - 1008},
	publisher = {MDPI},
	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.\&nbsp;\&nbsp;},
	doi = {10.3390/make3040049},
	url = {https://www.mdpi.com/2504-4990/3/4/49/pdf},
	author = {Joakim Olav {Valand} and Kadragic, Haris and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {28315,
	title = {Automated Clipping of Soccer Events using Machine Learning},
	journal = {IEEE International Symphosium of Multimedia (ISM)},
	year = {2021},
	month = {12/2021},
	publisher = { IEEE},
	doi = {10.1109/ISM52913.2021.00042},
	author = {Valand, Joakim and Kadragic, Haris and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
