Offensive Direction Inference in Real-World Football Video Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/cv43p0168

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  • Automatic analysis of American football videos can help teams develop strategies and extract patterns with less human effort. In this work, we focus on the problem of automatically determining which team is on offense/defense, which is an important subproblem for higher-level analysis. While seemingly mundane, this problem is quite challenging when the source of football video is relatively unconstrained, which is the situation we face. Our football videos are collected from a web-service used by more than 13, 000 high school, college, and professional football teams. The videos display huge variation in camera viewpoint, lighting conditions, football field properties, camera work, among many other factors. These factors taken together make standard off-the-shelf computer vision algorithms ineffective. The main contribution of this thesis is to design and evaluate two novel approaches for the offense/defense classification problem from raw video, which make minimal assumptions about the video properties. Our empirical evaluation on approximately 1200 videos of football plays from 10 diverse games validate the effectiveness of the approaches and highlight their differences.
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  • description.provenance : Submitted by Qingkai Lu (luq@onid.orst.edu) on 2015-06-11T23:04:55Z No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) LuQingkai2015.pdf: 5055405 bytes, checksum: ce21f6354579af8b66ca903ea6e17ca1 (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2015-06-17T16:14:57Z (GMT) No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) LuQingkai2015.pdf: 5055405 bytes, checksum: ce21f6354579af8b66ca903ea6e17ca1 (MD5)
  • description.provenance : Made available in DSpace on 2015-06-22T20:13:14Z (GMT). No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) LuQingkai2015.pdf: 5055405 bytes, checksum: ce21f6354579af8b66ca903ea6e17ca1 (MD5) Previous issue date: 2015-06-10
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2015-06-22T20:13:14Z (GMT) No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) LuQingkai2015.pdf: 5055405 bytes, checksum: ce21f6354579af8b66ca903ea6e17ca1 (MD5)

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