  Although human pose estimation for various computer vision (CV) applications
has been studied extensively in the last few decades, yet in-bed pose
estimation using camera-based vision methods has been ignored by the CV
community because it is assumed to be identical to the general purpose pose
estimation methods. However, in-bed pose estimation has its own specialized
aspects and comes with specific challenges including the notable differences in
lighting conditions throughout a day and also having different pose
distribution from the common human surveillance viewpoint. In this paper, we
demonstrate that these challenges significantly lessen the effectiveness of
existing general purpose pose estimation models. In order to address the
lighting variation challenge, infrared selective (IRS) image acquisition
technique is proposed to provide uniform quality data under various lighting
conditions. In addition, to deal with unconventional pose perspective, a 2-end
histogram of oriented gradient (HOG) rectification method is presented. In this
work, we explored the idea of employing a pre-trained convolutional neural
network (CNN) model trained on large public datasets of general human poses and
fine-tuning the model using our own shallow in-bed IRS dataset. We developed an
IRS imaging system and collected IRS image data from several realistic
life-size mannequins in a simulated hospital room environment. A pre-trained
CNN called convolutional pose machine (CPM) was repurposed for in-bed pose
estimation by fine-tuning its specific intermediate layers. Using the HOG
rectification method, the pose estimation performance of CPM significantly
improved by 26.4% in PCK0.1 criteria compared to the model without such
rectification.
