Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equipping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data for subsequent pose estimation. Our model transforms the ambiguous pressure maps to images containing shapes and structures similar to the common input domain of the pre-existing pose estimation methods. As a result, we show that our model is able to reconstruct unclear body parts, which in turn enables pose estimators to accurately and robustly estimate the pose. We train and test our method on a manually annotated public pressure map dataset using a combination of loss functions. Results confirm the effectiveness of our method by the high visual quality in the generated images and the high pose estimation rates achieved.
翻译:深造估计模型的最近进展证明在健康监测、体育、动画和机器人等广泛应用方面是有效的,但是,在面对从床面压力感测系统获得的图像时,提出的估计模型无法泛泛地概括。在本文件中,我们通过提出一个新的端对端框架来应对这一挑战,这个框架能够从模糊的压力数据中准确地定位身体部位。我们的方法利用了一个想法,即用一个深层可训练的神经网络来装备一个现成的表面显示仪,这个网络将预处理和准备压力数据,供随后的表面估计使用。我们的模型将模糊的压力图转换为含有与原存在的表面估计方法的共同输入领域类似的形状和结构的图像。结果,我们表明我们的模型能够重建不明的体部位,这反过来又能够使估计者准确和有力地估计其容貌。我们用损失功能的组合对人工加注的公众压力地图数据集进行培训和测试。结果通过生成图像的高视觉质量和所达到的高表面估计率证实了我们的方法的有效性。