AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI   Info

Public Audience

English

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Saarbrücken

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, e. 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.

Ellen Fries

9325-4003

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