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dc.contributor.authorShang, Jason
dc.date.accessioned2023-08-28 19:30:22 (GMT)
dc.date.available2023-08-28 19:30:22 (GMT)
dc.date.issued2023-08-28
dc.date.submitted2023-08-20
dc.identifier.urihttp://hdl.handle.net/10012/19782
dc.description.abstractHockey analytics involves obtaining information from games so that coaches, managers, and teams can make better decisions in training, playing, and hiring. As there is a large amount of information available in each game, manual analysis is difficult and tedious, so automated computer vision techniques have been developed to acquire and process data more efficiently. One key component to such analysis is the location information of players and events. This information can be obtained using a technique called rink registration, which involves estimating the homography matrix needed to warp an overhead template of the rink onto video frames, or vice versa. By doing this, we can obtain the location of objects in video with respect to the fixed reference frame of the overhead template. Current methods focus on NHL rinks, which have a standardized size and have similar appearances. However, the quality of results drop when other types of rinks are used, because the existing methods are not trained to work on non-NHL rinks. This work seeks to improve the rink registration process by making it more robust to differences in rinks, while maintaining good accuracy. It also tries to develop a generalized system that can work on a variety of rink types, such as NHL, Olympic, and European, without the need for additional rink-specific training or expensive annotations. By developing this rink-agnostic system, it can provide rink registration results regardless of rink, making analysis more equitable for smaller groups. It also reduces the cost needed as it only requires broadcast video and the overhead rink template, without the need for additional technology to be installed or annotations to be made. The results of this rink-agnostic system are competitive with the results of an NHL-only baseline on NHL rinks and are noticeably better than the baseline on non-NHL rinks. The rink-agnostic system achieves a 1.1% IOUpart improvement on the Olympic 2014 rink and a 8.8% IOUpart improvement on the Berlin Mercedes-Benz Arena rink.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectmachine learningen
dc.subjecthomographyen
dc.subjectcomputer visionen
dc.subjectdeep learningen
dc.subjectimage and video processingen
dc.subjectsegmentationen
dc.titleImproving Robustness of Homography Estimation for Ice Rink Registrationen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentSystems Design Engineeringen
uws-etd.degree.disciplineSystem Design Engineeringen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws-etd.embargo.terms0en
uws.contributor.advisorClausi, David
uws.contributor.advisorShafiee, Mohammad Javad
uws.contributor.affiliation1Faculty of Engineeringen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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