About: MOXA: A Deep Learning Based Unmanned Approach For Real-Time Monitoring of People Wearing Medical Masks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	MOXA: A Deep Learning Based Unmanned Approach For Real-Time Monitoring of People Wearing Medical Masks
Creator	Das, Tamodip Debarghya Dutta, · Debojit Ghosh, · Nandy, Subhadip Pritam Biswas, · Roy, Biparnak
Source	PMC
abstract	With 6.93M confirmed cases of COVID-19 worldwide, making individuals aware of their sanitary health and ongoing pandemic remains the only way to prevent the spread of this virus. Wearing masks is an important step in this prevention. Hence, there is a need for monitoring if people are wearing masks or not. Closed circuit television (CCTV) cameras endowed with computer vision function by embedded systems, have become popular in a wide range of applications, and can be used in this case for real time monitoring of people wearing masks or not. In this paper, we propose to model this task of monitoring as a special case of object detection. However, real-time scene parsing through object detection running on edge devices is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, we used a few popular object detection algorithms such as YOLOv3, YOLOv3Tiny, SSD and Faster R-CNN and evaluated them on Moxa3K benchmark dataset. The results obtained from these evaluations help us to determine methods that are more efficient, faster, and thus are more suitable for real-time object detection specialized for this task.
has issue date	2020-07-25(xsd:dateTime)
bibo:doi	10.1007/s41403-020-00157-z
has license	no-cc
sha1sum (hex)	3832c2d01065025b3e1c0edbd527663e07027379
schema:url	https://doi.org/10.1007/s41403-020-00157-z
resource representing a document's title	MOXA: A Deep Learning Based Unmanned Approach For Real-Time Monitoring of People Wearing Medical Masks
has PubMed Central identifier	PMC7382322
schema:publication	Trans Indian Natl
resource representing a document's body	covid:3832c2d01065025b3e1c0edbd527663e07027379#body_text
is schema:about of	named entity 'With' named entity 'embedded devices' named entity 'ongoing' named entity 'real-time' named entity 'popular' named entity 'EVALUATIONS' named entity 'USED' named entity 'DEVICES' named entity 'REAL TIME' named entity 'CLOSED CIRCUIT TELEVISION' named entity 'COVID-19' named entity 'PREVENT THE SPREAD' named entity 'DETERMINE' named entity 'METHODS' named entity 'POWER' named entity 'ALGORITHMS' named entity 'CHALLENGES' named entity 'SYSTEMS' named entity 'NEED FOR' named entity 'OBJECT DETECTION' named entity 'MONITORING' named entity 'DUE TO' named entity 'SSD' named entity 'AWARE' named entity 'RESULTS' named entity 'THEIR' named entity 'FASTER R-CNN' named entity 'CASE' named entity 'pandemic' named entity 'Faster' named entity 'SSD' named entity 'running' named entity 'real time' named entity 'evaluations' named entity 'monitoring' named entity 'masks' named entity 'Deep Learning' named entity 'CNN' named entity 'real-time' named entity 'object detection' named entity 'Real-Time Monitoring' named entity 'Medical Masks' named entity 'MOXA' named entity 'CCTV' named entity 'xml' named entity 'GPU' named entity 'CCTV camera' named entity 'object detection' named entity 'computing power' named entity 'object detection' named entity 'object detection' named entity 'processing units' named entity 'GPU' named entity 'main task' named entity 'CNN' named entity 'algorithm' named entity 'embedded systems' named entity 'facial detection' named entity 'boundary conditions' named entity '0.9' named entity 'learning rate' named entity 'input size' named entity 'video feed' named entity 'tensorflow' named entity '0.9' named entity 'YOLO' named entity 'CCTV camera' named entity 'learning rate' named entity 'CUDA' named entity 'data set' named entity 'learning rate' named entity 'object detection' named entity 'Xeon' named entity 'closed circuit television' named entity 'tensorflow' named entity 'Convolution' named entity 'tensorflow' named entity 'Darknet'

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