Author: Grewe, L.; Asati, S.; Choudhary, S.; Gallegos, E.; Gupta, D.; House, M.; Kes, C.; Ngyuen, J.; Patel, B.; Patel, K.; Pravin Jain, D.; Shahshahani, J.; Aguilera, P.; Shahshahani, A.; Rajiv Weginwar, M.; Hu, C.
Title: Health crisis situation awareness using mobile multiple modalities Cord-id: w3xmzzta Document date: 2021_1_1
ID: w3xmzzta
Snippet: 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 understand
Document: 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. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
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