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Scalable learning of actions from unlabeled videos

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dc.contributor.authorO'Hara, Stephen, author
dc.contributor.authorDraper, Bruce A., advisor
dc.contributor.authorHowe, Adele, committee member
dc.contributor.authorAnderson, Charles, committee member
dc.contributor.authorPeterson, Christopher, committee member
dc.date.accessioned2007-01-03T04:54:50Z
dc.date.available2007-01-03T04:54:50Z
dc.date.issued2013
dc.description.abstractEmerging applications in human-computer interfaces, security, and robotics have a need for understanding human behavior from video data. Much of the research in the field of action recognition evaluates methods using relatively small data sets, under controlled conditions, and with a small set of allowable action labels. There are significant challenges in trying to adapt existing action recognition models to less structured and larger-scale data sets. Those challenges include: the recognition of a large vocabulary of actions, the scalability to learn from a large corpus of video data, the need for real-time recognition on streaming video, and the requirement to operate in settings with uncontrolled lighting, a variety of camera angles, dynamic backgrounds, and multiple actors. This thesis focuses on scalable methods for classifying and clustering actions with minimal human supervision. Unsupervised methods are emphasized in order to learn from a massive amount of unlabeled data, and for the potential to retrain models with minimal human intervention when adapting to new settings or applications. Because many applications of action recognition require real-time performance, and training data sets can be large, scalable methods for both learning and detection are beneficial. The specific contributions from this dissertation include a novel method for Approximate Nearest Neighbor (ANN) indexing of general metric spaces and the application of this structure to a manifold-based action representation. With this structure, nearest-neighbor action recognition is demonstrated to be comparable or superior to existing methods, while also being fast and scalable. Leveraging the same metric space indexing mechanism, a novel clustering method is introduced for discovering action exemplars in data.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierOHara_colostate_0053A_11701.pdf
dc.identifier.urihttp://hdl.handle.net/10217/78864
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectaction recognition
dc.subjectapproximate nearest neighbor
dc.subjectGrassmann manifold
dc.subjectrandomized forests
dc.subjectunsupervised learning
dc.subjectvideo analysis
dc.titleScalable learning of actions from unlabeled videos
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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