| dc.description.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, 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. | en_US |
