Electrical and Computer Engineering ETDs
Publication Date
Fall 12-15-2016
Abstract
This thesis proposes an open-source, maintainable system for detecting human activity in large video datasets using scalable hardware architectures. The system is validated by detecting writing and typing activities that were collected as part of the Advancing Out of School Learning in Mathematics and Engineering (AOLME) project. The implementation of the system using Amazon Web Services (AWS) is shown to be both horizontally and vertically scalable. The software associated with the system was designed to be robust so as to facilitate reproducibility and extensibility for future research.
Keywords
cloud, human activity recognition, vertical scalability, horizontal scalability, AWS, distributed computing
Document Type
Thesis
Language
English
Degree Name
Computer Engineering
Level of Degree
Masters
Department Name
Electrical and Computer Engineering
First Committee Member (Chair)
Dr. Marios Pattichis
Second Committee Member
Dr. Ramiro Jordan
Third Committee Member
Dr. Wennie Shu
Recommended Citation
Eilar, Cody Wilson. "Distributed and Scalable Video Analysis Architecture for Human Activity Recognition Using Cloud Services." (2016). https://digitalrepository.unm.edu/ece_etds/300
Included in
Computer and Systems Architecture Commons, Education Commons, Electrical and Computer Engineering Commons
