Learning and Tracking Human Motion Using Functional Analysis

We present a method for the modeling and tracking of human motion using a sequence of 2D video images. Our analysis is divided in two parts: statistical learning and Bayesian tracking. First, we estimate a statistical model of typical activities from a large set of 3D human motion data. For this purpose, the human body is represented as a set of articulated cylinders and the evolution of a particular joint angle is described by a time-series. Specifically, we consider periodic motion such as “walking” in this work, and we develop a new set of tools that allows for the automatic segmentation of the training data into a sequence of identical “motion cycles”. Then we compute the mean and the principal components of these cycles using a new algorithm to account for missing informationand to enforce smooth transitions between different cycles. The learned temporal model provides a prior probability distribution over human motions which is used for tracking. We adopt a Bayesian perspective and approximate the posterior distribution of the body parameters using a particle filter. The resulting algorithm is able to track human subjects in monocular video sequences and to recover their 3D motion in complex unknown environments.