Responding to health crises requires the deployment of accurate and timely situation awareness. Understanding the location of geographical risk factors could assist in preventing the spread of contagious diseases and the system developed, Covid ID, is an attempt to solve this problem through the crowd sourcing of machine learning sensor-based health related detection reports. Specifically, Covid ID uses mobile-based Computer Vision and Machine Learning with a multi-faceted approach to understanding potential risks related to Mask Detection, Crowd Density Estimation, Social Distancing Analysis, and IR Fever Detection. Both visible-spectrum and LWIR images are used. Real results for all modules are presented along with the developed Android Application and supporting backend.
iSight is a mobile application to assist low vision people with the everyday task of sight. Specifically, the goal of the system is using 2D computer vision to refocus and visualize specific objects recognized in the image in an Augmented Reality scheme. This paper discusses the development of the application that uses a deep learning TensorFlow module to perform recognition of objects in the scene the user is looking at and consequently directs the formation of an augmented reality scene which is presented to the user to enhance their visual understanding. Both indoor and outdoor environments are tested and results are given. The success and challenges faced by iSight are presented along with future avenues of work.
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