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MeMoMan

MeMoMan

In a collaboration with the Ergonomics Department (Faculty of Mechanical Engineering) at the Technische Universität München we are developing new computational models and a system for accurate measurement of human motion. Our main goal is to develop markerless vision-based tracking algorithms for use with the industry-proven anthropometric human model RAMSIS. By providing RAMSIS with markerless tracking capabilities we hope to open up new fields of application in ergonomic studies and industrial design. On the other hand, we believe that a far-developed, flexible and accurate model such as RAMSIS is beneficial for human motion tracking given the ergonomic expertise that has affected its design.

Human Model

The human model consists of an inner model that is accurately modeled after a real human skeleton, and an outer model that can be adapted to different body types (anthropometries) and gender. It is parametrizable via the articulated joint angles of the inner model. Absolute motion limits in the joints ensure physiologically realistic postures. Originally consisting of 65 degrees of freedom (65 DOF), we have reduced the model to 51 DOF by applying an ergonomically sound interpolation of the spine joints.

Inner Model Outer Model
Figure: Inner model and corresponding joints (left) and outer model for different anthropometries and gender (right).

To improve performance of the model in tracking applications, we have incorporated optimizations such as caching of body part relative pose calculations and body part dependant inter-frame motion limits into the model.
We plan to extend the model with biomechanical preferences and cost functions related to internal/external forces as well as discomfort of the postures. Such extensions have already been presented in the original RAMSIS model.

Tracking

Tracking is performed in a Bayesian framework using a set of hierarchically coupled local particle filters. This makes it possible to sample efficiently from the high dimensional space of articulated human poses without constraining the allowed movements. Currently we are using a minimum of three cameras for tracking, to account for self-occlusions of the model. We will also investigate other setups, e.g. stereo cameras, to facilitate future use e.g. on mobile robots.
Our current research focuses on robust weight functions suitable for changing environments and on reliable motion prediction based on extracted image features. We are applying our methods in ergonomic studies conducted in collaboration with the TUM Ergonomics Department, as well as for the recognition of manipulation tasks in the Assistive Kitchen demonstration scenario of the CoTeSys cluster of excellence.

Here are some videos showing the performance of our approach on upper body motions (21 DOF) as well as full body motions (41 DOF):

upper body motion displaying outer model (size: 3MB)

upper body motion displaying inner model (size: 3MB)

full body motion (size: 36MB)

full body motion (size: 21MB)

Team

Publications

  • Jan Bandouch, Florian Engstler, and Michael Beetz. Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation. In Proceedings of the 19th British Machine Vision Conference (BMVC), 2008. Accepted for publication
  • Jan Bandouch, Florian Engstler, and Michael Beetz. Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model. In Proceedings of the Fifth International Conference on Articulated Motion and Deformable Objects (AMDO), 2008. Accepted for publication

Computer Science IX | Technische Universität München