Research Mentor(s)
Deneke, Wesley
Description
The field of human activity recognition from video data has recently made great strides. However, the large amount of labelled data needed to train activity recognition models remains a common bottleneck. We introduce a simulation platform to procedurally generate synthetic videos of household activities, which randomizes portions of the virtual scene like camera position, human model, and interaction motion to introduce video variation.
Document Type
Event
Start Date
15-5-2019 9:00 AM
End Date
15-5-2019 5:00 PM
Location
Carver Gym (Bellingham, Wash.)
Department
Computer Science
Genre/Form
student projects, posters
Subjects – Topical (LCSH)
Computer vision; Pattern recognition systems; Optical pattern recognition; Image processing; Marhine learning
Type
Image
Keywords
Computer Science, Unity, Procedural Generation
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf
Included in
A Simulation Platform for Generation of Synthetic Videos for Human Activity Recognition
Carver Gym (Bellingham, Wash.)
The field of human activity recognition from video data has recently made great strides. However, the large amount of labelled data needed to train activity recognition models remains a common bottleneck. We introduce a simulation platform to procedurally generate synthetic videos of household activities, which randomizes portions of the virtual scene like camera position, human model, and interaction motion to introduce video variation.