Every person spends around one third of his/her life in bed. For an infant or a young toddler, this percentage can be much higher, and for bed-bound patients it can go up to 100% of their time. In-bed pose estimation is a critical step in many human behavior monitoring systems that are focused on prevention, prediction, and management of at-rest or sleep-related conditions in both adults and children. The topic of automatic noncontact human pose estimation has received a lot of attention/success, especially in the last few years in the computer vision community, thanks to the introduction of deep learning and its power in artificial intelligence (AI) modeling. However, the state-of-the-art (SOTA) vision-based AI algorithms in this field can hardly work under the challenges associated with in-bed human behavior monitoring, such as significant illumination changes (e.g., full darkness at night), heavy occlusion (e.g., covering by a sheet or blanket), as well as the privacy concerns that mitigate large-scale data collection, necessary for any deep learning-based model training. The data quality challenges and privacy concerns have hindered the use of advanced vision-based in-bed behavior monitoring systems at home, which during the recent COVID-19 pandemic could have been an effective way to control the spread of the virus by avoiding in-person visits to clinics.

中文翻译：

---

保护隐私的床上人体姿势估计：IEEE 视频和图像处理杯 2021 学生竞赛亮点 [SP 竞赛]


每个人一生中约有三分之一的时间是在床上度过的。对于婴儿或幼儿来说，这一比例可能要高得多，而对于卧床不起的患者来说，这一比例可能高达 100%。床上姿势估计是许多人类行为监测系统的关键步骤，这些系统专注于预防、预测和管理成人和儿童的休息或睡眠相关状况。自动非接触式人体姿势估计这一主题受到了广泛的关注/成功，特别是在过去几年的计算机视觉社区中，这要归功于深度学习的引入及其在人工智能 (AI) 建模中的强大功能。然而，该领域最先进（SOTA）的基于视觉的人工智能算法很难应对与床上人类行为监控相关的挑战，例如显着的照明变化（例如夜间完全黑暗）、严重遮挡（例如，用床单或毯子覆盖），以及减轻大规模数据收集的隐私问题，这是任何基于深度学习的模型训练所必需的。数据质量挑战和隐私问题阻碍了先进的基于视觉的床上行为监测系统在家里的使用，在最近的 COVID-19 大流行期间，该系统可能是通过避免在家中控制病毒传播的有效方法。人到诊所就诊。