TY  - THES
AB  - This thesis deals with the problem of estimating and tracking the full articulation of
human hands. Algorithmically recovering hand articulations is a challenging problem
due to the hand’s high number of degrees of freedom and the complexity of its
motions. Besides the accuracy and efficiency of the hand posture estimation, hand
tracking methods are faced with issues such as invasiveness, ease of deployment
and sensor artifacts. In this thesis several different hand tracking approaches are examined,
including marker-based optical motion capture, data-driven discriminative
visual tracking and generative tracking based on articulated registration, and various
contributions to these areas are presented. The problem of optimally placing reduced
marker sets on a performer’s hand for optical hand motion capture is explored. A
method is proposed that automatically generates functional reduced marker layouts
by optimizing for their numerical stability and geometric feasibility. A data-driven
discriminative tracking approach based on matching the hand’s appearance in the
sensor data with an image database is investigated. In addition to an efficient nearest
neighbor search for images, a combination of discriminative initialization and
generative refinement is employed. The method’s applicability is demonstrated in
interactive robot teleoperation. Various real human hand motions are captured and
statistically analyzed to derive low-dimensional representations of hand articulations.
An adaptive hand posture subspace concept is developed and integrated into a generative
real-time hand tracking approach that aligns a virtual hand model with sensor
point clouds based on constrained inverse kinematics. Generative hand tracking is
formulated as a regularized articulated registration process, in which geometrical
model fitting is combined with statistical, kinematic and temporal regularization
priors. A registration concept that combines 2D and 3D alignment and explicitly accounts
for occlusions and visibility constraints is devised. High-quality, non-invasive,
real-time hand tracking is achieved based on this regularized articulated registration
formulation.
DA  - 2015
LA  - eng
PY  - 2015
TI  - Articulation estimation and real-time tracking of human hand motions
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:361-27773253
Y2  - 2024-11-24T23:10:22
ER  -