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A deep learning approach for action classification in American football video sequences
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The artificial intelligence is a constant topic of conversation with a field of research that is pushed forward by some of the world's largest companies and universities. Deep learning is a branch of machine learning within artificial intelligence based on learning representation of data such as images and texts by processing the data through deep neural networks. Sports are competitive businesses that over the years have become more data driven. Statistics play a big role in the development of the practitioners and the tactics in order to win. Sport organizations have big statistic teams since statistics are manually obtained by these teams. To learn a machine to recognize patterns and actions with deep learning would save a lot of time. In this thesis a deep learning approach is used to examine how well it can perform to classify the actions pass and run in American footbal lgames. A deep learning architecture is first trained and developed on a public video dataset and then trained to classify run and pass plays on a new American football dataset called the All-22 dataset. Results and earlier research show that deep learning has potential to automatize sport statistic but is not yet ready to overtake the role statistic teams have. Further research, bigger and more task specific datasets and more complex architectures are required to enhance the performance of this specific type of deep learning based video recognition.

Place, publisher, year, edition, pages
2017.
Series
UPTEC STS, ISSN 1650-8319 ; 17033
Keyword [en]
Deep Learning, Artificial Intelligence, American football
Keyword [sv]
Djupinlärning, Artificiell Intelligens, Amerikansk fotboll
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-333663OAI: oai:DiVA.org:uu-333663DiVA, id: diva2:1157319
Educational program
Systems in Technology and Society Programme
Supervisors
Examiners
Available from: 2017-11-17 Created: 2017-11-15 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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