Transactions of Nanjing University of Aeronautics & Astronautics
Infrared Thermography and Big Data for Detection of People with Fever and Determination of High-Risk Areas in Epidemic Situations
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
Technological advances in computer science and their application in our daily life allow us to improve our understanding of problems and solve them effectively. A system design to detect people with fever and determine high-risk areas using infrared thermography and big data is presented. In order to detect people with fever， face detection algorithms of Viola-Jones and Kanade-Lucas are investigated， and comparison between them is presented using a training set of 406 thermal images and a test set of 2 072 thermal images. Thermography analysis is performed on detected faces to obtain the temperature level on Celsius scale. With this information a sample database is created. To perform big data experimental analysis， Power Bi tool is used to determine the high-risk area. The experimental results show that Viola-Jones algorithm has a higher performance recognizing faces of thermal images than Kanade-Lucas， having a high detection rate， less false-positives rate and false-negatives rate.
Keywords:
Project Supported:
Clc Number:
TP391
Mobile website
