# Automatic Mobile Warning System against People with Elevated Body Temperature
###### Abstract  
The paper proposes a system that allows for the automatic detection of people with elevated body temperature and estimates distance from them using a smartphone-type device and a single mobile thermal camera. The algorithm automatically finds and selects humans with the highest temperature, and tracks changes in their position in an image sequence. On the basis of the change in the position of the human head in the image, in subsequent frames, the algorithm estimates the distance between camera and human. Owing to the use of fast machine-learning methods, the proposed system can immediately alert the user about the presence of a people with an elevated temperature at a distance of 1-3 m as soon as it appears in the field of view of the camera. The effectiveness of the algorithm was assessed as the ratio of correct distance classifications in the test image set to the total number of test images. Values ranging from 73% to 100% were obtained for over 4000 images of humans at different distances. The proposed method allows for the quick and completely automatic warning about people with elevated temperature, and can be used in popular Android mobile devices. thernvision image segmentation; object detection and localization; machine learning; computer vision +
Footnote †: journal:  
Modern smartphones or tablets to which the camera can be connected provide additional vast hardware capabilities that allow for the potential extension of the proposed solution, and the expansion of the image-analysis algorithm. Data from the thermal camera (in the form of temperature in degrees Celsius) in the observed area can be used to monitor hazards associated with elevated body temperature. The thermal camera mounted at chest height records the thermal image directly in front of the user and transmits it to the mobile monitoring application in which the measurement is made, and the degree of danger is determined. The algorithm (discussed below) implemented in the application allows for an appropriate reaction when a person with elevated temperature is detected. When objects with a temperature above 35 \({}^{\circ}\)C appear in the field of view, the system automatically detects them and determines the approximate distance from the object. The proposed solution assumes that the human head meets these assumptions, and temperature in its area exceeds 35 \({}^{\circ}\)C. In addition, if a temperature above 38 \({}^{\circ}\)C occurs in this area, the system may generate a warning about the danger associated with a person. The problem of determining distance is an important issue in image-analysis and object-tracking systems, and is widely discussed in the available literature. Most often, these solutions are used to monitor the distance between vehicles [6], assess the location of objects in automation and robotics systems [7], and reconstruct 3D scenes [8]. The proposed algorithms apply image-analysis methods and two main approaches, i.e., using one or two cameras [9]. It is relatively easy to use two cameras that, at the right location, provide images of the object from two different positions at the same time. Determining distance with this method is characterized by high efficiency. In [10], the authors proposed a driver-support system using stereoscopy and deep learning to detect and determine distance to a vehicle. Another example is described in [11], where two visible light cameras were used to estimate the distance. The known camera parameters, their mutual position, and trigonometric relationships were applied. Stereoscopy was also used in [12], where methods based on geometry and triangulation were used. When using a single camera, system costs are lower, but effectiveness may also be lower. Moreover, camera parameters have greater impact on accuracy.