English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Health organizations advise social distancing, wearing face mask, andavoiding touching face to prevent the spread of coronavirus. Based on theseprotective measures, we developed a computer vision system to help prevent thetransmission of COVID-19. Specifically, the developed system performs face maskdetection, face-hand interaction detection, and measures social distance. Totrain and evaluate the developed system, we collected and annotated images thatrepresent face mask usage and face-hand interaction in the real world. Besidesassessing the performance of the developed system on our own datasets, we alsotested it on existing datasets in the literature without performing anyadaptation on them. In addition, we proposed a module to track social distancebetween people. Experimental results indicate that our datasets represent thereal-world's diversity well. The proposed system achieved very high performanceand generalization capacity for face mask usage detection, face-handinteraction detection, and measuring social distance in a real-world scenarioon unseen data. The datasets will be available athttps://github.com/iremeyiokur/COVID-19-Preventions-Control-System.

Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19