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Visual analysis of high DOF articulated objects with application to hand tracking

Measurement of human hand and body motion is an important task for applications ranging from athletic performance analysis to advanced user interfaces. Commercial human motion sensors are invasive, requiring the user to wear gloves or targets. This thesis addresses noninvasive real-time 3D tracking of human motion using sequences of ordinary video images. Other computer vision systems have demonstrated real-time tracking of a single rigid object in six degrees-of-freedom (DOFs). Articulated objects like the hand present challenges to existing rigid-body tracking algorithms: a large number of DOFs (27 for the hand), nonlinear kinematic constraints, and complex self-occlusion effects. This thesis presents a novel local tracking framework based on explicit kinematic models that overcomes these obstacles. 
Kinematic models from robotics are used in this work to represent skeletal constraints and predict self-occlusions, which occur when one link of the object obscures the view of another. In local tracking, images are acquired at a high sampling rate and the linearized kinematic model is used to estimate the small motions between successive frames. Self-occlusion is modeled by a set of overlapping templates which are ordered by their visibility to the camera. This layered representation is constructed automatically from the kinematic model and used to constrain image interpretation. 
This thesis presents new experimental results for vision-based hand tracking. A real-time tracking system called DigitEyes was developed and tested in two experiments. First, a hand was tracked using a 27 DOF model and two cameras. Second, hand tracking with a single camera provided the input for a 3D mouse user-interface. Self-occluding hand motion was tracked off-line in a third experiment. These results illustrate the effectiveness of explicit kinematic models in constraining the interpretation of hand intensity images and analyzing self-occluding motion.