| Autor: |
Shivali Choudhary, Cemil Kes, Jake Shahshahani, Dikshant Pravin Jain, Maithri E. House, Divya Gupta, Allen Shahshahani, Subhangi Asati, Jamie Ngyuen, Emmanuel Gallegos, Manasi Rajiv Weginwar, Kunjkumar Patel, Chengzhi Hu, Bhumit Patel, Lynne L. Grewe, Phillip Aguilera |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Signal Processing, Sensor/Information Fusion, and Target Recognition XXX. |
| DOI: |
10.1117/12.2587544 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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