Estimating and Recognizing 3D Articulated Motion via Uncalibrated Cameras

这项工作的主要目标是从一个或多个未校准的视频流中开发用于3D关节结构和运动估计的新算法。 关节运动是由关节结构，如人体和手，以及更普遍的联系。 在这个项目中，三维关节结构和运动估计算法将开发，可以自动初始化自己，估计多个合理的解释沿着与他们的可能性，并提供可靠的性能超过扩展序列。 为了实现这些目标，将采用机器学习，图形模型，多视图几何和运动结构的概念。 拟议的研究工作将集中在两个主要领域：（1）从未校准的摄像机获得的视频中的3D关节姿态估计，（2）捕获关节运动动态的统计学习模型，以提供自上而下的指导，从而改善姿态估计并允许运动识别。 努力也将致力于研究改进的功能和图像分割方法中使用的前端系统。所开发的方法将在描述人体和人手运动的视频上进行测试，其中地面实况可用于定量比较。

The main goal of this effort is to develop new algorithms for 3D articulated structure and motion estimation, from one or more uncalibrated video streams. Articulated motion is exhibited by jointed structures like the human body and hands, as well as linkages more generally. In this project, 3D articulated structure and motion estimation algorithms will be developed that can automatically initialize themselves, estimate multiple plausible interpretations along with their likelihood, and provide reliable performance over extended sequences. To achieve these objectives, concepts from machine learning, graphical models, multiple view geometry, and structure from motion will be employed. The proposed research effort will focus in two main areas: (1) 3D articulated pose estimation given video obtained from uncalibrated cameras, (2) statistical learning models that capture the dynamics of articulated motion, to provide top-down guidance that is needed to improve the pose estimation and to allow motion recognition. Effort will also be devoted to investigating improved features and image segmentation methods for use in the front-end system. The developed methods will be tested on videos depicting motion of the human body and the human hand, where ground truth is available for quantitative comparison.