@inproceedings{5f0833f05e004a1eb40545728a18256f,
title = "Action Detection and Anomaly Analysis Visual System using Deep Learning for Robots in Pandemic Situation",
abstract = "In this paper a visual system equipped with state of the art in Deep Learning is presented which could be employed in the robotics platforms for the pandemic situation where human-human contact needs to be limited in order to perform various detection and anomaly analysis tasks. The developed detection and anomaly analysis system deals with human and environmental hazards and disasters especially for pandemic prevention. The system could detect whether the person of interest is wearing mask or not, social distancing is followed, person or environment are in normal condition for example if a window is open to keep ventilation in a closed environment. This research is a part of our project to develop a specific robot for pandemics to use advanced in artificial intelligence to make systems which could keep us safe and healthy. ",
keywords = "Anomaly Analysis, Coronavirus, Deep Learning, Detection, Pandemic, Robot, YOLO",
author = "Chung, {Chia Ling} and Chen, {Ding Bang} and Hooman Samani",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Automatic Control Conference, CACS 2020 ; Conference date: 04-11-2020 Through 07-11-2020",
year = "2020",
month = nov,
day = "4",
doi = "10.1109/CACS50047.2020.9289819",
language = "English",
series = "2020 International Automatic Control Conference, CACS 2020",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2020 International Automatic Control Conference, CACS 2020",
address = "United States",
}