Distributed video analysis for the advancing out of school learning in mathematics and engineering project
Cody Wilson Eilar, Venkatesh Jatla, Marios S. Pattichis, Carlos Alfonso LópezLeiva, Sylvia Celedón‐Pattichis · 2016
The paper proposes an open-source, maintainable system for detecting human activity in 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.