@inproceedings{700,
  author = {Kaiyuan Hou and Yanchen Liu and Peter Wei and Chenye Yang and Hengjiu Kang and Stephen Xia and Teresa Spada and Andrew Rundle and Xiaofan Jiang},
  title = {A Low-Cost In-situ System for Continuous Multi-Person Fever Screening},
  abstract = {With the recent societal impact of COVID-19, companies and government agencies alike have turned to thermal camera based skin temperature sensing technology to help screen for fever. However, the cost and deployment restrictions limit the wide use of these thermal sensing technologies. In this work, we present SIFTER, a low-cost system based on a RGB-thermal camera for continuous fever screening of multiple people. This system detects and tracks heads in the RGB and thermal domains and constructs thermal heat map models for each tracked person, and classifies people as having or not having fever. SIFTER can obtain key temperature features of heads in-situ at a distance and produce fever screening predictions in real-time, significantly improving screening through-put while minimizing disruption to normal activities. In our clinic deployment, SIFTER measurement error is within 0.4°F at 2 meters and around 0.6°F at 3.5 meters. In comparison, most infrared thermal scanners on the market costing several thousand dollars have around 1°F measurement error measured within 0.5 meters. SIFTER can achieve 100% true positive rate with 22.5% false positive rate without requiring any human interaction, greatly outperforming our baseline [1], which sees a false positive rate of 78.5%.},
  year = {2022},
  journal = {21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)},
  chapter = {15},
  pages = {13},
  month = {05},
  url = {https://par.nsf.gov/biblio/10362790},
  doi = {10.1109/IPSN54338.2022.00009},
}