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Football Shot Detection using Convolutional Neural Networks
Linköping University, Department of Biomedical Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architectures, different sampling methods are evaluated as well. This thesis shows that amongst the three evaluated methods, the ap- proach using MobileNetV2 as a feature extractor works best. However, when applying the networks to a video stream there are a multitude of challenges, such as false positives and incorrect annotations that inhibit the potential of detecting shots.

Place, publisher, year, edition, pages
2019. , p. 46
Keywords [en]
Convolution, Neural Network, Football, Action, Detection, MobileNet, Shot, 3D
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-157438ISRN: LIU-IMT/TFK-A-M--19/41--SEOAI: oai:DiVA.org:liu-157438DiVA, id: diva2:1323791
External cooperation
Signality AB
Subject / course
Computer science
Presentation
2019-06-05, Systemet, Linköping, 15:15 (English)
Supervisors
Examiners
Available from: 2019-06-14 Created: 2019-06-12 Last updated: 2019-06-14Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
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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