Measurement of human hand and body motion is an important task for applications in advanced user interfaces, motion capture, and athletic performance analysis. Historically, human motion sensors were invasive, and required the user to don gloves or wear targets. Recent developments in computer vision can provide noninvasive techniques for sensing human motion. Video cameras are passive, unobtrusive, and inexpensive, and they can be added to existing work environments with little modification. Human motion is governed by the kinematic constraints of the skeleton.
Skeletal kinematics provide the most basic source of information for visual tracking.
For my thesis, I explored the use of kinematic constraints in a direct, model-based tracking framework. Second, occlusions can be predicted from the kinematics, making tracking in the presence of self-occluding motion possible. This report introduces a local invariant of self-occluding motion and describes its use in tracking.