This paper presents a robust human posture and body parts detection method under a specific application scenario known as in-bed pose estimation. Although the human pose estimation for various computer vision (CV) applications has been studied extensively in the last few decades, the 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 problems. However, the in-bed pose estimation has its own specialized aspects and comes with specific challenges, including the notable differences in lighting conditions throughout the day and having pose distribution different from the common human surveillance viewpoint. In this paper, we demonstrate that these challenges significantly reduce the effectiveness of the existing general purpose pose estimation models. In order to address the lighting variation challenge, the infrared selective (IRS) image acquisition technique is proposed to provide uniform quality data under various lighting conditions. In addition, to deal with the unconventional pose perspective, a 2- end histogram of oriented gradient (HOG) rectification method is presented. The deep learning framework proves to be the most effective model in human pose estimation; however, the lack of large public dataset for in-bed poses prevents us from using a large network from scratch. In this paper, 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 (limited in size and different in perspective and color) 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 fine-tuned for in-bed pose estimation by re-training its specific intermediate layers. Using the HOG rectification method, the pose estimation performance of CPM improved significantly by 26.4% in the probability of correct key-point (PCK) criteria at PCK0.1 compared to the model without such rectification. Even testing with only well aligned in-bed pose images, our fine-tuned model still surpassed the traditionally tuned CNN by another 16.6% increase in pose estimation accuracy.

中文翻译：

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床上姿势估计：使用浅数据集进行深度学习

本文提出了一种在特定应用场景下的鲁棒人体姿势和身体部位检测方法，称为床上姿势估计。尽管在过去的几十年中已经对各种计算机视觉 (CV) 应用程序的人体姿态估计进行了广泛的研究，但 CV 社区忽略了使用基于相机的视觉方法进行的床上姿态估计，因为它被认为与通用姿势估计问题。然而，床上姿势估计有其自身的专业方面，并带来了特定的挑战，包括全天照明条件的显着差异以及与常见的人类监视观点不同的姿势分布。在本文中，我们证明这些挑战显着降低了现有通用姿态估计模型的有效性。为了解决光照变化的挑战，提出了红外选择性 (IRS) 图像采集技术，以在各种光照条件下提供均匀质量的数据。此外，为了处理非常规姿势视角，提出了一种定向梯度（HOG）校正方法的两端直方图。深度学习框架被证明是人体姿态估计中最有效的模型；然而，由于缺乏用于床上姿势的大型公共数据集，我们无法从头开始使用大型网络。在本文中，我们探索了采用在一般人体姿势的大型公共数据集上训练的预训练卷积神经网络 (CNN) 模型的想法，并在床上使用我们自己的浅层（大小有限，视角和颜色不同）微调模型国税局数据集。我们开发了一个 IRS 成像系统，并在模拟的病房环境中从几个逼真的真人大小的人体模型中收集 IRS 图像数据。通过重新训练其特定中间层，对称为卷积姿势机 (CPM) 的预训练 CNN 进行了微调，以进行床内姿势估计。使用 HOG 校正方法，与没有这种校正的模型相比，CPM 的姿态估计性能在 PCK0.1 的正确关键点 (PCK) 标准的概率方面显着提高了 26.4%。即使仅使用对齐良好的床上姿势图像进行测试，