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Analyzing Structured Scenarios by Tracking People and Their Limbs

dc.contributor.advisorDavis, Larry Sen_US
dc.contributor.authorMorariu, Vlad Ionen_US
dc.date.accessioned2011-02-19T06:52:23Z
dc.date.available2011-02-19T06:52:23Z
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/1903/11151
dc.description.abstractThe analysis of human activities is a fundamental problem in computer vision. Though complex, interactions between people and their environment often exhibit a spatio-temporal structure that can be exploited during analysis. This structure can be leveraged to mitigate the effects of missing or noisy visual observations caused, for example, by sensor noise, inaccurate models, or occlusion. Trajectories of people and their hands and feet, often sufficient for recognition of human activities, lead to a natural qualitative spatio-temporal description of these interactions. This work introduces the following contributions to the task of human activity understanding: 1) a framework that efficiently detects and tracks multiple interacting people and their limbs, 2) an event recognition approach that integrates both logical and probabilistic reasoning in analyzing the spatio-temporal structure of multi-agent scenarios, and 3) an effective computational model of the visibility constraints imposed on humans as they navigate through their environment. The tracking framework mixes probabilistic models with deterministic constraints and uses AND/OR search and lazy evaluation to efficiently obtain the globally optimal solution in each frame. Our high-level reasoning framework efficiently and robustly interprets noisy visual observations to deduce the events comprising structured scenarios. This is accomplished by combining First-Order Logic, Allen's Interval Logic, and Markov Logic Networks with an event hypothesis generation process that reduces the size of the ground Markov network. When applied to outdoor one-on-one basketball videos, our framework tracks the players and, guided by the game rules, analyzes their interactions with each other and the ball, annotating the videos with the relevant basketball events that occurred. Finally, motivated by studies of spatial behavior, we use a set of features from visibility analysis to represent spatial context in the interpretation of human spatial activities. We demonstrate the effectiveness of our representation on trajectories generated by humans in a virtual environment.en_US
dc.titleAnalyzing Structured Scenarios by Tracking People and Their Limbsen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pquncontrolledcomputer visionen_US
dc.subject.pquncontrolledevent recognitionen_US
dc.subject.pquncontrolledhuman activity understandingen_US
dc.subject.pquncontrolledspatio-temporal relationshipsen_US
dc.subject.pquncontrolledtrackingen_US
dc.subject.pquncontrolledvisibility analysisen_US


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