Human Motion Capture with Sparse Inertial Sensors and Video
Zusammenfassung
This thesis explores approaches to capture human motions with a small number of sensors. In the first part of this thesis an approach is presented that reconstructs the body pose from only six inertial sensors. Instead of relying on pre-recorded motion databases, a global optimization problem is solved to maximize the consistency of measurements and model over an entire recording sequence. The second part of this thesis deals with a hybrid approach to fuse visual information from a single hand-held camera with inertial sensor data. First, a discrete optimization problem is solved to automatically associate people detections in the video with inertial sensor
data. Then, a global optimization problem is formulated to combine visual and inertial information.
The propose approach enables capturing of multiple interacting people and works even if many more people are visible in the camera image. In addition, systematic inertial sensor errors can be compensated, leading to a substantial in...
Schlagworte
Human Pose Estimation Inertial Sensors Video Non-static Camera Modelbased Optimization Sparse Sensors- 5–21 1 Introduction 5–21
- 22–48 2 Fundamentals 22–48
- 103–106 5 Conclusions 103–106
- 107–120 Bibliography 107–120