Distance Estimation in Thermal Cameras Using Multi-Task Cascaded Convolutional Neural Network

Abstract

The rapid growth of the current pandemic (COVID-19) requires the use of thermal cameras that can perform fast and automatic body temperature measurement. The accuracy of the temperature measurement is affected by its distance from a person. Conventional distance estimation methods utilize the coordinates of the bounding box provided by several face detection algorithms such as YOLOv3 and SSD. The bounding box output of these methods varies which causes inaccurate distance estimation results. In this study, we propose a distance estimation method for thermal camera applications based on the coordinates of the facial key points extracted using multi-task cascaded convolutional neural network. The result obtained in this study proves that the proposed method exhibits higher accuracy (root mean square error of 2.9695 cm in comparison with an RMSE of 25.26 cm using other methods) and the least CPU and memory consumption in comparison with conventional methods.