Multi-person pose estimation using thermal images

Abstract

Human pose estimation is an important task for several applications, such as video surveillance systems. However, color (i.e., RGB) images may not always be available under certain conditions, such as privacy issues and lack of illumination. In these scenarios, thermal images are more prominent than color images. We introduce in this study ThermalPose, which is a neural network system that parses thermal images and extracts accurate 2D human poses. ThermalPose uses lightweight neural network models that can be easily matched to the design requirements for Internet-of-Things applications. The performance of ThermalPose in visible scenes only slightly decreases compared with that of the state-of-the-art vision-based pose estimator. Meanwhile, in complex scenes with masking background textures or lack of illumination, ThermalPose does not degenerate in terms of performance, whereas the vision-based system completely fails.