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- Info
MeMoMan
Markerless Tracking of Unconstrained Human Motions in Everyday (Living) Environments
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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
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Human Motion Capture, Tracking and Analysis
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Probabilistic Model Fitting
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 several videos to showcase the performance and different aspects of our work:
Selected Publications
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Jan Bandouch
,
Michael Beetz
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Tracking Humans Interacting with the Environment Using Efficient Hierarchical Sampling and Layered Observation Models,
2009,
IEEE Int. Workshop on Human-Computer Interaction (HCI). In conjunction with ICCV2009,
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Moritz Tenorth
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Jan Bandouch
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Michael Beetz
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The {TUM} Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition,
2009,
IEEE Int. Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS). In conjunction with ICCV2009,
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Florian Engstler,
Jan Bandouch
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Heiner Bubb,
MeMoMan - Model Based Markerless Capturing of Human Motion,
2009,
The 17th World Congress on Ergonomics (International Ergonomics Association, IEA),
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Jan Bandouch
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Florian Engstler,
Michael Beetz
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Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation,
2008,
Proceedings of the 19th British Machine Vision Conference (BMVC),
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Jan Bandouch
,
Florian Engstler,
Michael Beetz
,
Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model,
2008,
Proceedings of the Fifth International Conference on Articulated Motion and Deformable Objects (AMDO),
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Michael Beetz
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Freek Stulp
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Bernd Radig
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Jan Bandouch
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Nico Blodow
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Mihai Emanuel Dolha
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Andreas Fedrizzi
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Dominik Jain
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Ulrich Klank
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Ingo Kresse
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Alexis Maldonado
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Zoltan-Csaba Marton
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Lorenz Mösenlechner
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Federico Ruiz-Ugalde
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Radu Bogdan Rusu
,
Moritz Tenorth
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The Assistive Kitchen --- A Demonstration Scenario for Cognitive Technical Systems,
2008,
IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany,
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