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% Search: % Encoding: utf-8 @InProceedings{beetz07assistive, author = {Michael Beetz and Jan Bandouch and Alexandra Kirsch and Alexis Maldonado and Armin M{\"u}ller and Radu Bogdan Rusu}, title = {The Assistive Kitchen --- A Demonstration Scenario for Cognitive Technical Systems}, booktitle = {Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM)}, year = {2007}, bib2html_pubtype ={Workshop Paper}, bib2html_rescat ={Planning,Learning,Action,Perception,Models,Reasoning}, bib2html_groups ={Cogito,Memoman,Cogman}, abstract = { This paper introduces the Assistive Kitchen as a comprehensive demonstration and challenge scenario for technical cognitive systems. We describe its hardware and software infrastructure. Within the Assistive Kitchen application, we select particular domain activities as research subjects and identify the cognitive capabilities needed for perceiving, interpreting, analyzing, and executing these activities as research foci. We conclude by outlining open research issues that need to be solved to realize the scenarios successfully.} } @InProceedings{beetz08assistive, author = {Michael Beetz and Freek Stulp and Bernd Radig and Jan Bandouch and Nico Blodow and Mihai Dolha and Andreas Fedrizzi and Dominik Jain and Uli Klank and Ingo Kresse and Alexis Maldonado and Zoltan Marton and Lorenz M{\"o}senlechner and Federico Ruiz and Radu Bogdan Rusu and Moritz Tenorth}, title = {{The Assistive Kitchen -- A Demonstration Scenario for Cognitive Technical Systems}}, booktitle = {IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany}, pages = {1-8}, year = {2008}, bib2html_pubtype = {Conference Paper}, bib2html_rescat = {Perception, Planning, Learning}, bib2html_groups = {Memoman, Cogito, EnvMod, Cogman, K4C, ProbCog}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, note = {Invited paper.}, } @InProceedings{bandouch08bmvc, author = {Jan Bandouch and Florian Engstler and Michael Beetz}, title = {{Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation}}, year = {2008}, booktitle = {Proceedings of the 19th British Machine Vision Conference (BMVC)}, bib2html_pubtype ={Conference Paper}, bib2html_rescat = {Perception}, bib2html_groups ={Memoman}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, abstract = {A common approach to the problem of 3D human pose estimation from video is to recursively estimate the most likely pose via particle filtering. However, standard particle filtering methods fail the task due to the high dimensionality of the 3D articulated human pose space. In this paper we present a thorough evaluation of two variants of particle filtering, namely Annealed Particle Filtering and Partitioned Sampling Particle Filtering, that have been proposed to make the problem feasible by exploiting the hierarchical structures inside the pose space. We evaluate both methods in the context of markerless model-based 3D motion capture using silhouette shapes from multiple cameras. For that we created a simulation from ground truth sequences of human motions, which enables us to focus our evaluation on the sampling capabilities of the approaches, i.e. on how efficient particles are spread towards the modes of the distribution. We show the behaviour with respect to the amount of cameras used, the amount of particles used, as well as the dimensionality of the search space. Especially the performance when using more complex human models (40 DOF and above) that are able to capture human movements with higher precision compared to previous approaches is of interest in this work. In summary, we show that both methods have complementary strengths, and propose a combined method that is able to perform the tracking task with higher robustness despite reduced computational effort.} } @InProceedings{bandouch08amdo, author = {Jan Bandouch and Florian Engstler and Michael Beetz}, title = {Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model}, year = {2008}, booktitle = {Proceedings of the Fifth International Conference on Articulated Motion and Deformable Objects (AMDO)}, bib2html_pubtype ={Conference Paper}, bib2html_rescat = {Perception}, bib2html_groups = {Memoman}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, abstract = {In this paper we present our work on markerless model-based 3D human motion capture using multiple cameras. We use an industry proven anthropometric human model that was modeled taking ergonomic considerations into account. The outer surface consists of a precise yet compact 3D surface mesh that is mostly rigid on body part level apart from some small but important torsion deformations. Benefits are the ability to capture a great amount of possible human appearances with high accuracy while still having a simple to use and computationally efficient model. We have introduced special optimizations such as caching into the model to improve its performance in tracking applications. Available force and comfort measures within the model provide further opportunities for future research. 3D articulated pose estimation is performed in a Bayesian framework, using a set of hierarchically coupled local particle filters for tracking. This makes it possible to sample efficiently from the high dimensional space of articulated human poses without constraining the allowed movements. Sequences of tracked upper-body as well as full-body motions captured by three cameras show promising results. Despite the high dimensionality of our model (51 DOF) we succeed at tracking using only silhouette overlap as weighting function due to the precise outer appearance of our model and the hierarchical decomposition.} } @InProceedings{Rusu08ROMAN, author = {Radu Bogdan Rusu and Jan Bandouch and Zoltan Csaba Marton and Nico Blodow and Michael Beetz}, title = {{Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences}}, booktitle = {IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany}, year = {2008}, bib2html_pubtype = {Conference Paper}, bib2html_rescat = {Perception}, bib2html_groups = {Memoman, EnvMod}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, abstract = { In this paper we present our work on human action recognition in intelligent environments. We classify actions by looking at a time-sequence of silhouettes extracted from various camera images. By treating time as the third spatial dimension we generate so-called space-time shapes that contain rich information about the actions. We propose a novel approach for recognizing actions, by representing the shapes as 3D point clouds and estimating feature histograms for them. Preliminary results show that our method robustly derives different classes of actions, even in the presence of large variability in the data, coming from different persons at different time intervals. } } @InProceedings{bandouch09hci, author = {Jan Bandouch and Michael Beetz}, title = {Tracking Humans Interacting with the Environment Using Efficient Hierarchical Sampling and Layered Observation Models}, booktitle = {IEEE Int. Workshop on Human-Computer Interaction (HCI). In conjunction with ICCV2009}, year = {2009}, bib2html_pubtype ={Conference Paper}, bib2html_rescat = {Perception}, bib2html_groups = {Memoman}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, abstract = { We present a markerless tracking system for unconstrained human motions which are typical for everyday manipulation tasks. Our system is capable of tracking a high-dimensional human model (51 DOF) without constricting the type of motion and the need for training sequences. The system reliably tracks humans that frequently interact with the environment, that manipulate objects, and that can be partially occluded by the environment. We describe and discuss two key components that substantially contribute to the accuracy and reliability of the system. First, a sophisticated hierarchical sampling strategy for recursive Bayesian estimation that combines partitioning with annealing strategies to enable efficient search in the presence of many local maxima. Second, a simple yet effective appearance model that allows for the combination of shape and appearance masks to implicitly deal with two cases of environmental occlusions by (1) subtracting dynamic non-human objects from the region of interest and (2) modeling objects (e.g. tables) that both occlude and can be occluded by human subjects. The appearance model is based on bit representations that makes our algorithm well suited for implementation on highly parallel hardware such as commodity GPUs. Extensive evaluations on the HumanEva2 benchmarks show the potential of our method when compared to state-of-the-art Bayesian techniques. Besides the HumanEva2 benchmarks, we present results on more challenging sequences, including table setting tasks in a kitchen environment and persons getting into and out of a car mock-up.} } @InProceedings{tenorth09dataset, author = {Moritz Tenorth and Jan Bandouch and Michael Beetz}, title = {{The {TUM} Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition}}, booktitle = {IEEE International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS), in conjunction with ICCV2009}, year = {2009}, bib2html_pubtype ={Conference Paper}, bib2html_rescat = {Perception}, bib2html_groups = {Memoman, K4C}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, abstract = {We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, fullbody motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations.} } @InProceedings{beetz09qlts, author = {Michael Beetz and Jan Bandouch and Dominik Jain and Moritz Tenorth}, title = {{Towards Automated Models of Activities of Daily Life}}, booktitle = {First International Symposium on Quality of Life Technology -- Intelligent Systems for Better Living}, year = {2009}, address = {Pittsburgh, Pennsylvania USA}, bib2html_pubtype ={Conference Paper}, bib2html_rescat = {Learning,Models,Planning,Perception,Knowledge,Reasoning,Representation}, bib2html_groups = {Memoman,K4C,ProbCog}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, abstract = {We propose automated probabilistic models of everyday activities (AM-EvA) as a novel technical means for the perception, interpretation, and analysis of everyday manipulation tasks and activities of daily life. AM-EvAs are based on action-related concepts in everyday activities such as action-related places (the place where cups are taken from the cupboard), capabilities (the objects that can be picked up single-handedly), etc. These concepts are probabilistically derived from a set of previous activities that are fully and automatically observed by computer vision and additional sensor systems. AM-EvA models enable robots and technical systems to analyze activities in the complete situation and activity context. They render the classification and the assessment of actions and situations objective and can justify the probabilistic interpretation with respect to the activities the concepts have been learned from. In this paper, we describe the current state of implementation of the system that realizes this idea of automated models of everyday activities and show example results from the observation and analysis of table setting episodes.} } @Article{Rusu09RSJ-AR, author = {Radu Bogdan Rusu and Jan Bandouch and Franziska Meier and Irfan Essa and Michael Beetz}, title = {{Human Action Recognition using Global Point Feature Histograms and Action Shapes}}, journal = {Advanced Robotics journal, Robotics Society of Japan (RSJ)}, year = {2009}, bib2html_pubtype = {Journal}, bib2html_rescat = {Perception}, bib2html_groups = {Memoman, EnvMod}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, abstract = { This article investigates the recognition of human actions from 3D point clouds that encode the motions of people acting in sensor-distributed indoor environments. Data streams are time-sequences of silhouettes extracted from cameras in the environment. From the 2D silhouette contours we generate space-time streams by continuously aligning and stacking the contours along the time axis as third spatial dimension. The space-time stream of an observation sequence is segmented into parts corresponding to subactions using a pattern matching technique based on suffix trees and interval scheduling. Then, the segmented space-time shapes are processed by treating the shapes as 3D point clouds and estimating global point feature histograms for them. The resultant models are clustered using statistical analysis, and our experimental results indicate that the presented methods robustly derive different action classes. This holds despite large intra-class variance in the recorded datasets due to performances from different persons at different time intervals. } } @InProceedings{engstler09iea, author = {Florian Engstler and Jan Bandouch and Heiner Bubb}, title = {MeMoMan - Model Based Markerless Capturing of Human Motion}, booktitle = {The 17th World Congress on Ergonomics (International Ergonomics Association, IEA)}, year = {2009}, address = {Beijing, China}, bib2html_pubtype = {Conference Paper}, bib2html_rescat = {Perception}, bib2html_groups = {Memoman}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household} } @article{beetz10ameva, title={{Towards Automated Models of Activities of Daily Life}}, author={Michael Beetz and Moritz Tenorth and Dominik Jain and Jan Bandouch}, journal={Technology and Disability}, volume={22}, number= {1-2}, pages={27--40}, year={2010}, publisher={IOS Press}, bib2html_pubtype = {Journal}, bib2html_groups = {K4C, Memoman, ProbCog}, bib2html_funding = {CoTeSys, MeMoMan}, bib2html_rescat = {Perception, Models, Representation, Learning, Reasoning}, bib2html_domain = {Assistive Household} } @article{Bandouch12memoman, author = {Jan Bandouch and Odest Chadwicke Jenkins and Michael Beetz}, title = {A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns}, journal = {International Journal of Computer Vision}, volume = {99}, number = {2}, year = {2012}, pages = {166-189} }