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Abstract

The processing of human motion data constitutes an important strand of research
with many applications
in computer animation, sport science and medicine. Currently, there exist
various systems for recording
human motion data that employ sensors of different modalities such as optical,
inertial and
depth sensors. Each of these sensor modalities have intrinsic advantages and
disadvantages that
make them suitable for capturing specific aspects of human motions as, for
example, the overall
course of a motion, the shape of the human body, or the kinematic properties of
motions.
In this thesis, we contribute with algorithms that exploit the respective
strengths of these
different modalities for comparing, classifying, and tracking human motion in
various scenarios.
First, we show how our proposed techniques can be employed, \textite.\,g.,
for real-time motion reconstruction
using efficient cross-modal retrieval techniques. Then, we discuss a practical
application of inertial sensors-based
features to the classification of trampoline motions. As a further contribution,
we elaborate on estimating the human body shape from depth data with
applications to personalized motion tracking.
Finally, we introduce methods to stabilize a depth tracker in challenging
situations such as in presence of occlusions.
Here, we exploit the availability of complementary inertial-based sensor
information.