Real-Time Human Pose Recognition in Parts from Single Depth Images
Real-Time Human Pose Recognition in Parts from Single Depth Images
复制标题
DOI：
10.1145/2398356.2398381
复制
发表时间：
2013-01-01
影响因子：
22.7
通讯作者：
Moore, Richard
中科院分区：
文献类型：
--
作者：
Shotton, Jamie;Sharp, Toby;Moore, Richard
我们提出了一种新的方法，可以快速准确地从单幅深度图像中预测人体姿势--身体关节的3D位置--而不依赖于之前帧的信息。我们的方法深深植根于当前的目标识别策略。通过设计身体部位的中间表示，将困难的位姿估计问题转化为更简单的单像素分类问题，并提供了有效的机器学习技术。通过使用计算机图形学来合成训练图像对的非常大的数据集，人们可以训练分类器，该分类器从测试图像中估计身体部位标签不随姿势、体型、服装和其他无关而变化。最后，通过对分类结果的重新投影和局部模式的寻找，生成了多个人体关节的可信度3D方案，系统在Xbox 360上的运行时间不超过5ms。我们的评估在合成测试集和真实测试集上都显示了很高的准确率，并考察了几个训练参数的影响。在与相关工作的比较中，我们获得了最先进的准确率，并证明了改进的泛化优于精确的全骨架最近邻匹配。
We propose a new method to quickly and accurately - predict human pose-the 3D positions of body joints-from a single depth image, without depending on information from preceding frames. Our approach is strongly rooted in current object recognition strategies. By designing an intermediate - representation in terms of body parts, the difficult pose estimation problem is transformed into a simpler per-pixel classification problem, for which efficient machine learning techniques exist. By using computer graphics to synthesize a very large dataset of training image pairs, one can train a classifier that estimates body part labels from test images invariant to pose, body shape, clothing, and other irrelevances. Finally, we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.The system runs in under 5ms on the Xbox 360. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state-of-the-art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.