Summary of Thermal Vision: Pioneering Non-invasive Temperature Tracking in Congested Spaces, by Arijit Samal et al.
Thermal Vision: Pioneering Non-Invasive Temperature Tracking in Congested Spaces
by Arijit Samal, Haroon R Lone
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of non-invasive temperature monitoring, specifically for individuals in close proximity, such as movie theaters or classrooms. The existing research has focused on sparse settings, but the risk of disease transmission is higher in dense environments. To address this, the authors develop robust temperature estimation methods tailored for dense settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep people safe by developing a way to measure body temperatures without touching anyone. Right now, most research focuses on taking temperatures in places where people are far apart. But what if we’re trying to take temperatures in crowded areas like movie theaters or classrooms? The risk of spreading diseases is much higher there! So, the authors are working on ways to do temperature checks that work really well in these kinds of situations. |
Keywords
» Artificial intelligence » Temperature
