IEEE Trans Image Process. 2013 Dec;22(12):4627-39. doi: 10.1109/TIP.2013.2274748. Epub 2013 Jul 24.
Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the image data to achieve a more robust body pose tracking. Various body prior models have been proposed. Most of them are data-driven, typically learned from 3D motion capture data. In addition to being expensive and time-consuming to collect, these data-based prior models cannot generalize well to activities and subjects not present in the motion capture data. To alleviate this problem, we propose to learn the prior model from anatomic, biomechanics, and physical constraints, rather than from the motion capture data. For this, we propose methods that can effectively capture different types of constraints and systematically encode them into the prior model. Experiments on benchmark data sets show the proposed prior model, compared with data-based prior models, achieves comparable performance for body motions that are present in the training data. It, however, significantly outperforms the data-based prior models in generalization to different body motions and to different subjects.
基于视频的人体姿势估计旨在从图像或视频序列中估计人体姿势，该图像或视频序列捕捉到一个人正在进行某些活动。为了处理噪声和遮挡问题，通常会构建姿势先验模型，然后将其与从图像数据中估计的姿势相结合，以实现更稳健的人体姿势跟踪。已经提出了各种身体先验模型。它们大多数都是数据驱动的，通常是从 3D 运动捕捉数据中学习而来的。除了收集起来昂贵且耗时之外，这些基于数据的先验模型不能很好地推广到运动捕捉数据中不存在的活动和主体。为了解决这个问题，我们建议从解剖学、生物力学和物理约束中学习先验模型，而不是从运动捕捉数据中学习。为此，我们提出了一些方法，可以有效地捕获不同类型的约束，并将其系统地编码到先验模型中。在基准数据集上的实验表明，与基于数据的先验模型相比，所提出的先验模型在针对训练数据中存在的身体运动的性能方面表现相当，但在推广到不同的身体运动和不同的主体方面，它的表现明显优于基于数据的先验模型。
