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Classifying sport videos with deep neural networks
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This project aims to apply deep neural networks to classify video clips in applications used to streamline advertisements on the web. The system focuses on sport clips but can be expanded into other advertisement fields with lower accuracy and longer training times as a consequence. The main task was to find the neural network model best suited for classifying videos. To achieve this the field was researched and three network models were introduced to see how they could handle the videos. It was proposed that applying a recurrent LSTM structure at the end of an image classification network could make it well adapted to work with videos. The most popular image classification architectures are mostly convolutional neural networks and these structures are also the foundation of all three models. The results from the evaluation of the models as well as the research suggests that using a convolutional LSTM can bean efficient and powerful way of classifying videos. Further this project shows that by reducing the size of the input data with 25%, the training and evaluation time can be cut with around 50%. This comes at the cost of lower accuracy. However it is demonstrated that the performance loss can be compensated by considering more frames from the same videos during evaluation.

Place, publisher, year, edition, pages
2017. , 88 p.
Series
UMNAD, 1092
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-130654OAI: oai:DiVA.org:umu-130654DiVA: diva2:1069116
External cooperation
Codemill
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2017-01-27 Created: 2017-01-27Bibliographically approved

Open Access in DiVA

fulltext(2754 kB)197 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
  • rtf