Exploring Human-Object Interaction Detection

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dc.contributor.advisorShi, Humphrey
dc.contributor.authorBergstrom, Trevor
dc.date.accessioned2020-12-08T15:48:16Z
dc.date.available2020-12-08T15:48:16Z
dc.date.issued2020-12-08
dc.description.abstractHuman-object interaction detection is a relatively new task in the world of computer vision and visual semantic information extraction. The goal of human-object interaction detection is to have machines identifying interactions that humans perform on objects. We provide a basic survey of the developments in the field of human object interaction detection. Many works in this field use multi-stream convolutional neural network architectures, which combine features from multiple sources in the input image. To provide insight to future researchers, we perform a study examining the performance of each component of a multi-stream architecture for human-object interaction detection. We examine the HORCNN architecture as a foundational work in the field. We also provide an in-depth look at the HICO-DET dataset, a popular benchmark in the field of human object interaction detection. Lastly, we begin the construction of a human-object interaction benchmarking platform.en_US
dc.identifier.urihttps://hdl.handle.net/1794/25903
dc.language.isoen_US
dc.publisherUniversity of Oregon
dc.rightsAll Rights Reserved.
dc.subjectComputer-visionen_US
dc.subjecthuman-object interaction detectionen_US
dc.subjectMachine Learningen_US
dc.titleExploring Human-Object Interaction Detection
dc.typeElectronic Thesis or Dissertation
thesis.degree.disciplineDepartment of Computer and Information Science
thesis.degree.grantorUniversity of Oregon
thesis.degree.levelmasters
thesis.degree.nameM.S.

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