In-Pose Estimation of Covered and Uncovered Human Body from Thermal Camera Images Using Multi-Scale Stacked Hourglass (MSSHg) Network

Abstract

Estimating the pose of the human body lying on the bed has enormous and valuable benefits in various applications such as medicine and healthcare. Regular and long-term monitoring and detection of human poses in bed need sustaining certain poses, as they contribute to better recovery after certain surgeries or to control the symptoms effects of many complications. Particularly, when the body is fully covered or in completely dark ambient, existing methods for in-bed pose monitoring should be upgraded or new methods should be developed. An economical, contactless, vision-based system that is not sensitive to both challenges of darkness and cover is a thermal camera imaging system using the long-wavelength IR technique. In this regard, the dataset used in this study is the SLP data set, in which several different modes of poses with various covers are collected simultaneously. This dataset is a thermal camera-based dataset and is entirely annotated as an in-bed pose dataset. In this paper, the multi-scale stacked Hourglass (MSSHg) network, has been applied to improve the processing of thermal camera images for human pose estimation. Drawing on the concept of multi-scale, the aim of using the pre-processing network in this model is to extract feature maps with different scales and assign them to different stacked Hourglass networks. The performance of this approach for in-bed pose estimation showed progress with the result accuracy of 96.8% in PCK0.2 standard. In comparison with stacked Hourglass network results, the MSSHg network has about 0.8% more accuracy for covered body pose estimation and as much as or even higher accuracy for uncovered body pose estimation at the final stage. Additionally, it converges faster and easier at the beginning of the training stage.