Human Body Parsing in Thermal InfraRed Domain

Abstract

Thermal InfraRed (TIR) technology has achieved significant progress, in light of its ability to reflect lighting conditions in dark environments, enhancing its vital role in industrial and consumer electronics. However, current research on TIR image semantic segmentation mainly focuses on urban scenes, while the segmentation of human bodies in the TIR domain remains an under-explored area, which holds considerable promise for applications such as low-light security checks, nocturnal combat scenarios, and human action recognition. In this paper, we introduce a novel computer-vision task—Thermal InfraRed Human Body Parsing, which aims to generate accurate segmentation maps for different parts of human bodies in TIR images. To open up future research in this area, we collect a new dataset called HBTIR, which contains TIR images and corresponding semantic labels of 32 participants in various poses. Furthermore, we propose a novel neural network called HBTIR-Seg, which incorporates an edge-guided attention mechanism specifically tailored for TIR human body imagery. Extensive experiments demonstrate that our method greatly outperforms existing segmentation methods on the HBTIR dataset.