MeMoMan
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We are developing new computational models and a system for accurate measurement of human motion. Our primary goal is to develop markerless vision-based tracking algorithms for use with the industry-proven anthropometric human model RAMSIS (in collaboration with the TUM Ergonomics Department/Faculty of Mechanical Engineering). By providing RAMSIS with markerless tracking capabilities, we 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.
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Research Topics
- Human Motion Capture, Tracking and Analysis
- Probabilistic Model Fitting
Application Domain
- Intelligent Kitchen
Project Details
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.
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 several sequences:
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short presentation video on kitchen sequence |
a longer presentation video |
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our results on the HumanEva2 benchmark |
car mock-up video |
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a 6.5 minute sequence tracked at once |
effect of environment modeling on tracking |
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kitchen sequence with random |
another kitchen sequence with random |
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setting a table, different actor |
two subjects in a joint action scenario |
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21 DOF upperbody only motions showing the accuracy of the model |
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Selected Publications
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MeMoMan - Model Based Markerless Capturing of Human Motion, 2009, The 17th World Congress on Ergonomics (International Ergonomics Association, IEA),(BibTeX)
