MoLa IR CovSurv

Published: 20 December 2021| Version 1 | DOI: 10.17632/rgg6b7tx4s.1


This repository presents one of the datasets described in the article "AI based monitoring of different risk levels in Covid19 context", published in the Multidisciplinary Digital Publishing Institute special issue "Human Activity Recognition Based on Image Sensors and Deep Learning". The repository includes the complete dataset used for the training, validation and testing tasks, in order to detect the presence ou absence of mask and glasses on people, and detect the caruncle zone of people's eyes (both on a thermografic context). For the detection of mask and glasses, there are two different folders: images and labels, each divided in three different subdatasets (train, valid, test). For each image, there is a text document with the exactly same name, where is present the information about each object (in this case, people's faces). This labels information uses the class associated to the object (0: Face_Mask_Eyes, 1: Face_Mask_NoEyes, 2: Face_NoMask_Eyes, 3: Face_NoMask_NoEyes, where Eyes its related to person without glasses and NoEyes related to person with glasses), and the correspondent normalized values of the bounding box of the face (x_center, y_center, width, height). For the detection of the caruncle zone, the image folder has the same format as the previous task, while the labels are shown in JSON format, one for each subdataset, where are presented the bounding box of the face and the pair of coordinates (x, y) of both caruncles.



Universidade do Minho Escola de Engenharia


Supervised Learning, Human, Deep Learning, COVID-19