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dc.contributor.advisorHoff, William A.
dc.contributor.advisorZhang, Hao
dc.contributor.authorReily, Brian J.
dc.date.accessioned2016-05-24T22:45:54Z
dc.date.accessioned2022-02-03T12:58:09Z
dc.date.available2016-05-24T22:45:54Z
dc.date.available2022-02-03T12:58:09Z
dc.date.issued2016
dc.identifierT 8032
dc.identifier.urihttps://hdl.handle.net/11124/170153
dc.descriptionIncludes bibliographical references.
dc.description2016 Spring.
dc.descriptionRevised 2018.
dc.description.abstractDepth imagery is transforming many areas of computer vision, such as object recognition, human detection, human activity recognition, and sports analysis. The goal of my work is twofold: (1) use depth imagery to effectively analyze the pommel horse event in men’s gymnastics, and (2) explore and build upon the use of depth imagery to recognize human activities through skeleton representation. I show that my gymnastics analysis system can accurately segment a scene based on depth to identify a ‘depth of interest’, ably recognize activities on the pommel horse using only the gymnast’s silhouette, and provide an informative analysis of the gymnast’s performance. This system runs in real-time on an inexpensive laptop, and has been built into an application in use by elite gymnastics coaches. Furthermore, I present my work expanding on a bio-inspired skeleton representation obtained through depth data. This representation outperforms existing methods in classification accuracy on benchmark datasets. I then show that it can be used to interact in real-time with a Baxter humanoid robot, and is more accurate at recognizing both complete and ongoing interactions than current state-of-the-art methods.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2016 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectactivity prediction
dc.subjectactivity recognition
dc.subjectdepth imagery
dc.subjectgymnastics
dc.subjectimage segmentation
dc.titleHuman activity recognition and gymnastics analysis through depth imagery
dc.typeText
dc.contributor.committeememberWang, Hua
dc.contributor.committeememberCelik, Ozkan
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineElectrical Engineering and Computer Science
thesis.degree.grantorColorado School of Mines


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