Automatic bone parameter estimation for skeleton tracking in optical motion capture

Motion analysis is important in a broad range of contexts, including animation, bio-mechanics, robotics and experiments investigating animal behavior. For applications, in which tracking accuracy is one of the main requirements, passive optical motion capture systems are widely used. Many skeleton tracking methods based on such systems use a predefined skeleton model, which is scaled once in the initialization step to the individual size of the character to be tracked. However, there are remarkable differences in the bone length relations across gender and even more across mammal races. In practice, the optimal skeleton model has to be determined in a manual and time-consuming process. In this paper, we reformulate this task as an optimization problem aiming to rescale a rough hierarchical skeleton structure to optimize probabilistic skeleton tracking performance. We solve this optimization problem by means of state-of-the-art blackbox optimization methods based on sequential model-based Bayesian optimization (SMBO). We compare different SMBO methods on three real-world datasets with an animal and humans, demonstrating that we can automatically find skeleton structures for previously unseen mammals. The same methods also allow an automated choice of a suitable starting frame for initializing tracking.

Authors

• Tobias Schubert 0002
• Katharina Eggensperger
• Alexis Gkogkidis
• Frank Hutter
• Tonio Ball
• Wolfram Burgard

Keywords

• Bones
• Tracking
• Animals
• Optimization
• Muscles
• Manuals
• Motion Capture
• Optical Motion Capture
• Skeleton Tracking
• Optimization Problem
• Time-consuming Process
• Tracking Performance
• Motion Analysis
• Bayesian Optimization
• Bone Length
• Skeleton Structure
• Broad Range Of Contexts
• Optical Motion Capture System
• Objective Function
• Performance Measures
• Optimal Function
• Global Optimization
• Number Of Markers
• Error Function
• Tracking Error
• Predictive Distribution
• Inertial Measurement Unit
• Manual Measurements
• Marker Positions
• Spearmint
• Acquisition Function
• Random Search
• Passive Markers
• Manual Labeling
• Starting Values
• Human Female