Computer Vision for Medical Infant Motion Analysis: State of the Art and RGB-D Data Set
Computer Vision for Medical Infant Motion Analysis: State of the Art and RGB-D Data Set
复制标题
用于医学婴儿运动分析的计算机视觉：最先进的技术和 RGB-D 数据集
DOI：
10.1007/978-3-030-11024-6_3
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Schroeder
中科院分区：
文献类型：
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作者：
Nikolas Hesse;C. Bodensteiner;Michael Arens;U. Hofmann;Raphael Weinberger;A. Schroeder
对婴儿自发运动的评估可以让训练有素的专家在很小的时候就预测像脑瘫这样的神经发育障碍，从而对受影响的婴儿进行早期干预。自动运动分析系统需要准确地捕捉身体运动，理想情况下没有标记或附加的传感器，以不影响婴儿的运动。绝大多数最近的人类姿势估计方法集中在成人身上，如果应用于婴儿，会导致准确性下降。因此，已经开发了用于婴儿姿势估计的多个系统。由于缺乏公开的基准数据集，不可能进行标准化评价，更不用说对不同方法进行比较了。我们通过发布RGB-D（MINI-RGBD）中的移动婴儿（数据集可用于http：//s.fhg.de/mini-rgbd）数据集来填补这一空白，该数据集使用最近引入的皮肤多婴儿线性身体模型（SMIL）创建。我们将真实的婴儿运动映射到具有逼真形状和纹理的SMIL模型，并生成具有精确地面真实2D和3D关节位置的RGB和深度图像。我们使用最先进的方法评估我们的数据集，用于RGB图像中的2D姿态估计和深度图像中的3D姿态估计。2D姿态估计的评估导致PCKh率分别为88.1%和94.5%（取决于正确性阈值），以及3D姿态估计的PCKh率分别为64.2%和90.4%。我们希望促进医学婴儿运动分析的研究，以更接近早期检测神经发育障碍的自动化系统。
Assessment of spontaneous movements of infants lets trained experts predict neurodevelopmental disorders like cerebral palsy at a very young age, allowing early intervention for affected infants. An automated motion analysis system requires to accurately capture body movements, ideally without markers or attached sensors to not affect the movements of infants. A vast majority of recent approaches for human pose estimation focuses on adults, leading to a degradation of accuracy if applied to infants. Hence, multiple systems for infant pose estimation have been developed. Due to the lack of publicly available benchmark data sets, a standardized evaluation, let alone a comparison of different approaches is impossible. We fill this gap by releasing the Moving INfants In RGB-D (MINI-RGBD) (Data set available for research purposes at http://s.fhg.de/mini-rgbd) data set, created using the recently introduced Skinned Multi-Infant Linear body model (SMIL). We map real infant movements to the SMIL model with realistic shapes and textures, and generate RGB and depth images with precise ground truth 2D and 3D joint positions. We evaluate our data set with state-of-the-art methods for 2D pose estimation in RGB images and for 3D pose estimation in depth images. Evaluation of 2D pose estimation results in a PCKh rate of 88.1% and 94.5% (depending on correctness threshold), and PCKh rates of 64.2%, respectively 90.4% for 3D pose estimation. We hope to foster research in medical infant motion analysis to get closer to an automated system for early detection of neurodevelopmental disorders.