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ORCID
https://orcid.org/0000-0002-3516-4713
Access Type
Campus-Only Access for Five (5) Years
Document Type
thesis
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
2019
Month Degree Awarded
September
Abstract
We present a video-based system designed to enhance the efficiency of emergency preparedness and recovery. Our system includes two tools, one or emergency preparedness, and the other for emergency recovery.
In our emergency preparedness tool, we 1) capture videos from the physical environment (e.g., University Campus, Knowles Engineering Building, North Station Subway Station), 2) generate an immersive virtual environment using these videos, 3) provide exploration and navigation mode in the virtual environment, and 4) allow users to make annotations of the environment and review these created annotations. By using this virtual environment, emergency personnel can familiarize themselves with the environment before their actual training in the physical setting. This tool can enhance the training experience of emergency responders as well as improve the training outcomes.
Our emergency recovery tool includes two parts: 1) an Android application which captures the videos in the disaster site, and 2) a server that stores all these videos and provides visual analytics support. The recorded videos (both in indoor and outdoor settings), which include location and orientation information, are uploaded to the server that supports spatial queries. We developed tools for the investigators to review these videos, e.g., review videos in specific areas of interest.
To test our tools, we select both outdoor and indoor environments. The results show that our tool works well in these situations.
DOI
https://doi.org/10.7275/15321693
First Advisor
Aura Ganz
Recommended Citation
Yin, Juechen, "A Video-Based System for Emergency Preparedness and Recovery" (2019). Masters Theses. 867.
https://doi.org/10.7275/15321693
https://scholarworks.umass.edu/masters_theses_2/867