Pose estimation and tracking has been a focus of computer vision research for many years. Despite many successes, however, most approaches to date are still not able to recover physically realistic (natural looking) 3d motions and are restricted to captures indoors or with simplified backgrounds. In the first part of this talk, I will briefly introduce a class of models that use physics to constrain the motion of the subject to more realistic interpretations.
In particular, we formulate the pose tracking problem as one of inference of control mechanisms which implicitly (through physical simulation) generate the kinematic motion matching the image observations. This formulation of the problem has a number of benefits with respect to more traditional kinematic models. In the second part of the talk, I will describe a new proof-of-concept framework for capturing human motion in outdoor environments where traditional motion capture systems, including marker-less motion systems, would typically be inapplicable.
The proposed system consists of a number of small body-mounted cameras, placed on all major segments of the body, and is capable of recovering the underlying skeletal motion by observing the scene as it changes, within each camera view, with the motion of the subjects’ body.