dc.contributor.advisor |
Shi, Humphrey |
|
dc.contributor.author |
Bergstrom, Trevor |
|
dc.date.accessioned |
2020-12-08T15:48:16Z |
|
dc.date.available |
2020-12-08T15:48:16Z |
|
dc.date.issued |
2020-12-08 |
|
dc.identifier.uri |
https://scholarsbank.uoregon.edu/xmlui/handle/1794/25903 |
|
dc.description.abstract |
Human-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.language.iso |
en_US |
|
dc.publisher |
University of Oregon |
|
dc.rights |
All Rights Reserved. |
|
dc.subject |
Computer-vision |
en_US |
dc.subject |
human-object interaction detection |
en_US |
dc.subject |
Machine Learning |
en_US |
dc.title |
Exploring Human-Object Interaction Detection |
|
dc.type |
Electronic Thesis or Dissertation |
|
thesis.degree.name |
M.S. |
|
thesis.degree.level |
masters |
|
thesis.degree.discipline |
Department of Computer and Information Science |
|
thesis.degree.grantor |
University of Oregon |
|