The proposed model is the first dyadic interaction localizer in a multi-person setting, which enables it to be used in public spaces to identify handshake interactions and thereby identify and mitigate COVID-19 transmission.
no code implementations • 21 Aug 2021 • Umar Marikkar, Harshana Weligampola, Rumali Perera, Jameel Hassan, Suren Sritharan, Gihan Jayatilaka, Roshan Godaliyadda, Vijitha Herath, Parakrama Ekanayake, Janaka Ekanayake, Anuruddhika Rathnayake, Samath Dharmaratne
In this study, a forecasting solution is proposed, to predict daily new cases of COVID-19 in regions small enough where containment measures could be locally implemented, by targeting three main shortcomings that exist in literature; the unreliability of existing data caused by inconsistent testing patterns in smaller regions, weak deploy-ability of forecasting models towards predicting cases in previously unseen regions, and model training biases caused by the imbalanced nature of data in COVID-19 epi-curves.
There is a lack of unsupervised learning approaches for decomposing an image into reflectance and shading using a single image.
We show that a fully functional DeepLight system is able to robustly achieve high decoding accuracy (frame error rate < 0. 2) and moderately-high data goodput (>=0. 95Kbps) using a human-held smartphone camera, even over larger screen-camera distances (approx =2m).
Low light image enhancement is an important challenge for the development of robust computer vision algorithms.
This paper presents a novel algorithm for the detection of sleep apnea with video processing.