PROMETHEUS
WP5 – Behavior Modeling
Kamrad Khoshhal Roudposhti
Version: 2.6
Mobile Robotics Laboratory
Institute of Systems and Robotics
ISR – Coimbra
INDEX
1- Objectives
2- Human behavior analysis
3- Behavior samples
4- Other projects in around of the our subjects
5- Reference
6- Planning for WP5
7- Reference
Mobile Robotics Laboratory
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Objectives
Mobile Robotics Laboratory
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Summary of WP5
START: T0+12 (January 2009)
Involved partners
Person - months
FOI,
UOP,
TUM,
2
11
15
ISR-FCTUC,
20
PROBAYES,
10
TEIC
3
Tasks
Task 5.1 :
Particle filtering techniques applied to the learning process of Bayesian network
structures
Task 5.2 :
Learning/Recognition of Human Action/ Interaction patterns
Task 5.3 :
Short time Prediction of Human Intention
Deliverables
D5.1 (T0+24) Progress on Behaviour Modelling (TEIC)
D5.2 (T0+30) Learning and short-term prediction (FCTUC)
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Objectives
The scope of WP5 :
•analysis and recognition of motion patterns and the production of
high-level description of actions and interactions.
•Understanding of behaviors
Specifically, this WP must conclude on
–a) represent semantic concepts of behavior,
–b) map motion characteristics -mainly velocities and feature trajectories- to
semantic concepts
–c) choose efficient representations to interpret the scene meanings.
The detection is based on matching observed behavior with the
learned patterns.
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Human behavior analysis
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What´s the subject
•Human behavior is the collection of behaviors exhibited by human beings
and influenced by culture, attitudes, emotions, ....(From Wikipedia)
•The behavior of people falls within a range with some behavior being
common, some unusual, some acceptable, and some outside acceptable
limits.(From Wikipedia)
•Many researchers worked on special human behaviour from
several categories. Some of popular subjects about this area in
the world are: gait [Dawson], action analysis [C. Rao et al.], gesture
recognition [Mitra and Acharya], and facial expression recognition
[Bartlett] and explicit body movement based communication, namely
sign language recognition [Kadir et al.] and etc.
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•Prerequisite of human behavior is human motion.
•Wang showed a general framework for different levels vision analysis that it shows in a
Figure: [Wang et al]
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Laban Movement Analysis
•Laban Movement Analysis (LMA) is a method for observing,
describing, notating, and interpreting human movement
•The works of Norman Badler's group mention 5 major
components shown in Figure,
The major components of LMA are Body, Space, Effort, Shape and
Relationship
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Laban Movement Analysis
Space
The Space component defines several concepts: a) Levels of Space, Basic Directions,Three
Axes, and b) Three Planes Door Plane (vertical), Table plane (horizontal) , and the Wheel
Plane (sagittal) each one lying in two of the axes (Joerg Rett and Jorge Dias 2007)
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Laban Movement Analysis
Effort
•
•
•
•
Space: Direct / Indirect
Weight: Strong / Light
Time: Sudden / Sustained
Flow: Bound / Free
Effort
Movement
Space Direct
Pointing gesture
Space Indirect
Waving away bugs
Weight Strong
Punching
Weight Light
Dabbing paint on a canvas
Time Sudden
Swatting a fly
Time Sustained
Stretching to yawn
Flow Bound
Moving in slow motion
Flow Free
Waving wildly
Effort qualities and exemplary movements
(Jörg Rett and Jorge Dias 2007)
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Laban Movement Analysis
Shape
•
Rett summarize three Shape qualities and express it in terms of spatial
directions. By using a major and a minor direction we are able to express the
Shape in the concept of the Three Planes (πvert , π horz,π sag).
Shape
Direction
example
Plane
Enclosing
Major: Sideward
Clasping someone in a hug
Horizontal
Spreading
Minor: For-/Backward
Opening arms to embrace
Sinking
Major: Up-/Downward
Stamping the floor indignation
Rising
Minor: sideward
Reaching for something in a high shelf
Retreating
Major: For-/Backward
Avoiding a punch
Advancing Minor: Up-/Downward
Vertical
Sagittal
Reaching forward to shake hands
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State of the art
•A long tradition in research on computational solutions for Laban Movement Analysis (LMA)
has the group around Norman Badler, who already started in 1993 to re-formulate
Labanotation in computational models [Badler1993].
•The work of Zhao & Badler [Zhao&Badler] is entirely embedded in the framework of Laban
Movement Analysis. Their computational model of gesture acquisition and synthesis can
be used to learn motion qualities from live performance. Many inspirations concerning the
transformation of LMA components into physically measurable entities were taken from this
work.
Trajectories of sensors (attached at shoulders,
elbows, and hands).
L. Zhao, N.I. Badler / Graphical Models 67
(2005)
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State of the art
•Nakata et al. reproduced expressive movements in a robot that could be interpreted as
emotions by a human observer. [Nakata]
•The first part described how some parameters of Laban Movement Analysis (LMA) can be
calculated from a set of low-level features.
•They concluded further that the control of robot movements oriented on LMA parameters
allows the production of expressive movements and that those movements leave the
impression of emotional content to a human observer.
• The critical points on the mapping of low-level features to LMA parameters was, that the
computational model was closely tied to the embodiment of the robot which had only a low
number of degrees of freedom.
2 dimensional shape
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State of the art
•Rett & Dias in [Rett&Dias2007] presented as a contribution to the field of human-machine
interaction (HMI) a system that analyzes human movements online, based on the concept
of Laban Movement Analysis (LMA).
•The implementation used a Bayesian model for learning and classification.
•They presented the Laban Movement Analysis as a concept to identify useful
features of human movements to classify human actions.
•The movements were extracted using both, vision and magnetic tracker.
•The descriptor opened possibilities towards expressiveness and emotional content.
•To solve the problem of classification, they used the Bayesian framework as it offers an
intuitive approach to learning and classification.
The components and the frames of reference for tracking human movements
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Behavior Samples
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Indoor Part
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Place down bag
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Pick up bag
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Robbery
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Falling down
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Faint & robbery
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Faint (SmartHome)
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Fighting
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Greeting
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Panic situation
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Lift and move a heavy box and...
The person falls down
The normal action
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Outdoor Part
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Fighting & Pushing
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2 Persons Fighting and one of them escape
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An angry person
An normal person
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Loitering
Normal
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Other projects in around of the our subjects
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The COBOL project
Communication with Emotional Body Language
•COmmunication with Emotional BOdy Language (COBOL) was
launched in 2006 by the the European Commission as project, in
the 6th EU framework programme. The Commission will be
supporting this Specific Targeted Research Project for three
years, to the tune of €1.8 million.
The project consists of 5 workpackages, each of which is described below
Workpackage 1:
Description and analysis of the kinematic and dynamical structure of EBL
Workpackage 2:
Development of EBL avatars and measurement of EBL perception and recognition
Workpackage 3:
The cognitive basis of EBL
Workpackage 4:
Coordinating social interactions
Workpackage 5:
Cross-cultural EBL
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The AMI project
Augmented Multi-party Interaction
•The AMI Consortium formed in January 2004 to
conduct basic and applied research, with the aim of
developing technologies that help people have more
productive meetings.
•Their technologies rely on basic research in disciplines
ranging from speech recognition, language processing,
computer vision, human-human communication
modeling, and multimedia indexing and retrieval. The
AMI Consortium brings together scientists from these
fields as well as technologists, interface specialists,
and social psychologists in order to achieve its vision.
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Netcarity
Ambient technology to support older people at home
Netcarity was launched in 2007 and for 4
years, €13 million European project
researching and testing technologies which will
help older people improve their:
•Wellbeing
•Independence
•Safety
•Health
Mobile Robotics Laboratory
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References from the Partners
Mobile Robotics Laboratory
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Reference from Partners
• [1] Markus Ablassmeier, Tony Poitschke, Frank Wallho, Klaus Bengler, and Gerhard Rigoll. Eye gaze studies comparing head-up and head-down
displays in vehicles. pages 2250 2252. Multimedia and Expo, 2007 IEEE International Conference, July 2007.
• [2] Jorgen Ahlberg, Martin Folkesson, Christina Gronwall, Tobias Horney, Erland Jungert, Lena Klasen, and Morgan Ulvklo. Ground target
recognition in a query- ased multi-sensor information system. Technical Report LiTH-ISY-R-2748, Division of Automatic Control Department of
Electrical Engineering Linkopings universitet, October 2006.
• [3] Simon Ahlberg, Pontus Horling, Katarina Johansson, Karsten Jored, Hedvig Kjellstrom, Christian Martenson, Goran Neider, Johan Schubert,
Pontus Svenson, Per Svensson, and Johan Walter. An information fusion demonstrator for tactical intelligence processing in network-based
defense. Inf. Fusion, 8(1):84107, 2007.
• [4] Marc Al-Hames, Benedikt H²ornler, Ronald M²uller, Joachim Schenk, and Gerhard Rigoll. Automatic multi-modal meeting camera selection for
videoconferences and meeting browsers. pages 2074 2077. Multimedia and Expo, 2007 IEEE International Conference, July 2007.
• [5] Dejan Arsic, Joachim Schenk, Bjorn Schuller, Frank Wallho, and Gerhard Rigoll. Submotions for hidden markov model based dynamic facial
action recognition. pages 673 676. Image Processing, 2006 IEEE International Conference on 8-11 Oct. 2006, Oct. 2006.
• [6] Dejan Arsi.c, Frank Wallho, Bj²orn Schuller, and Gerhard Rigoll. Video based online behavior detection using probabilistic multi stream fusion.
pages 1354 1357. Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on 6-8 July 2005, July 2005.
• [7] Dejan Arsi.c, Frank Wallho, Bj²orn Schuller, and Gerhard Rigoll. Video based online behavior detection using probabilistic multi stream fusion.
Volume 2, pages 6069. Image Processing, 2005. ICIP 2005. IEEE International Conference on Volume 2, 11-14 Sept. 2005, Sept. 2005.
• [8] Mikael Brännström, Ron Lennartsson, Andris Lauberts, Hans Habberstad, Erland Jungert, and Martin Holmberg. Distributed data fusion in a
ground sensor network. Stockholm, Sweden, July 2004. The 7th International Conference on Information Fusion June 28 to July 1, 2004.
• [9] C. Coué, Th. Fraichard, P. Bessiere, and E. Mazer. Multi-sensor data fusion using bayesian programming : an automotive application.
• [10] C. Coué, Th. Fraichard, P. Bessikre, and E. Maze. Using bayesian programming for multi-sensor multi - target tracking in automotive
applications. Proceedings of the 2003 IEEE ,International Conference on Robotics &Au- tomation, 2003.
• [11] C. Coué, Th. Fraichard, P. Bessière, and E. Mazer. Multi-sensor data fusion using bayesian programming : an automative application.
Conference on intelligent Robots and Systems EPFL, Lausanne, Switzerland ., Proceesings of the 2002 IEEE/RSJ, October 2002.
• [12] Julien Diard, Pierre Bessière, and Emmanuel Mazer. Merging probabilistic models of navigation: the bayesian map and the superposition
operator. This work is supported by the BIBA european project (IST-2001-32115)., 2001.
• [13] Fadi Dornaika and Jorgen Ahlberg. Fitting 3d face models for tracking and active appearance model training. Image and Vision Computing 24,
pages 1010 1024, 2006.
• [14] Theodoros Giannakopoulos, Nicolas Alexander Tatlas, Todor Ganchev, and Ilyas Potamitis. A practical, real-time speech-driven home
automation front-end. IEEE Transactions on Consumer Electronics, 51, MAY 2005.
• [15] T. Kostoulas, I. Mporas, T. Ganchev, and N. Fakotakis. The e ect of emotional speech on a smart-home application. 21st International
Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, 2008.
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Reference from Partners
• [16] A. Lazaridis, T. Kostoulas, I. Mporas, T. Ganchev, N. Katsaounos, S. Ntalampiras, and N. Fakotakis. Human evaluation of the logos
multimodal dialogue system. Athens, Greece, July 2008. 1st International Conference on PErvasive Technologies Related to Assistive
Environments July 16 - 19.
• [17] Anna Linderhed, Stefan Sjökvist, Sten Nyberg, Magnus Uppsäll, Christina Grönwall, Pierre Andersson, and Dietmar Letalick.
Temporal analysis for land mine detection. Proceedings of the 4th International Symposium on Image and Signal Processing and
Analysis (2005), 2005.
• [18] I. Potamitis, N. Fakotakis, and G. Kokkinakis. Robust automatic speech recognition in the presence of impulsive noise.
ELECTRONICS LETTERS 7th June 2007, 37, June 2007.
• [19] Ilyas Potamitis. Estimation of speech presence probability in the eld of microphone array. IEEE SIGNAL PROCESSING LETTERS, 11,
DECEMBER 2004.
• [20] Ilyas Potamitis, Huimin Chen, and George Tremoulis. Tracking of multiple moving speakers with multiple microphone arrays. IEEE
TRANS- ACTIONS ON SPEECH AND AUDIO PROCESSING, 12, SEPTEMBER 2004.
• [21] Ilyas Potamitis and George Kokkinakis. Speech separation of multiple moving speakers using multisensor multitarget techniques.
IEEE TRANS- ACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART A: SYS- TEMS AND HUMANS, 37, JANUARY 2007.
• [22] Stephan Reiter, Bj²orn Schuller, and Gerhard Rigoll. Segmentation and recognition of meeting events using a two-layered hmm and a
combined mlp-hmm approach. pages 953 956. Multimedia and Expo, 2006 IEEE International Conference on 9-12 July 2006, July 2006.
• [23] J. Rett and J. Dias. Human robot interaction based on bayesian analysis of human movements. Proceedings of EPIA 07, Lecture
Notes in AI, Springer Verlag, Berlin., 2007.
• [24] Joerg Rett, Jorge Dias, and Juan-Manuel Ahuactzin. Laban Movement Analysis using a Bayesian model and perspective projections.
Brain, Vision and AI, 2008. ISBN: 978-953-7619-04-6.
• [25] Jörg Rett. ROBOT-HUMAN Interface using LABAN Movement Analysis Inside a Bayesian framework. PhD thesis, University of
Coimbra, 2008.
• [26] Gerhard Rigoll, Stefan Eickeler, and Stefan M²uller. Person tracking in realworld scenarios using statistical methods. pages 398 402.
Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on 28-30 March 2000, March 2000.
• [27] Sascha Schreiber, Andre Stormer, and Gerhard Rigoll. A hierarchical asm/aam approach in a stochastic framework for fully
automatic tracking and recognition. pages 1773 1776. Image Processing, 2006 IEEE International Conference on 8-11 Oct. 2006, Oct.
2006.
• [28] Frank Wallho, Martin RuB, Gerhard Rigoll, Johann Gobel, and Hermann Diehl. Surveillance and activity recognition with depth
information. Pages 1103 1106. Multimedia and Expo, 2007 IEEE International Conference on 2-5 July 2007, July 2007.
• [29] N. Xiong and P. Svensson. Multi-sensor management for information fusion: issues and approaches. Information Fusion 3, pages
163186, 2002.
• [30] P. Zervas, N. Fakotakis, and G. Kokkinakis. Development and evaluation of a prosodic database for greek speech synthesis and
research. Journal of Quantitative Linguistics, 15(2):154184, 2008.
Mobile Robotics Laboratory
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Planning for WP5
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Relationship of the WP3,4 and 5
WP3
(Sensor modeling and multi-sensor fusion techniques )
WP4
(Localization and tracking techniques as applied to humans )
Task 3.3
Bayesian network structures
for multi-modal fusion
Task 4.3
Online adaptation and learning
WP5
Behavior learning and recognition
Mobile Robotics Laboratory
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This WP includes:
•Application of particle filtering in the inference procedure of
Bayesian network structures including the novel cases of Multi
stream, Coupled and Asynchronous HMMs
•Training of Bayesian network structures on ground truth of the
perceptual modalities, which will be available from hand-labeled
data, and recognition of behavior
•Evaluation of the efficiency of Bayesian network structures on
generating short-term prediction of tasks based on the
observations of the multi-modal network
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Partner
Specific Skills
FOI
project management, surveillance systems, data fusion, people tracking,
assessment of different types of threats, potential of new sensors [17, 3, 2, 8, 13, 29]
UOP
acoustics, speech recognition, speech understanding, natural language
processing, microphone arrays, speaker localization and tracking, speaker
verification and identification, language recognition [15, 16, 30]
TUM
human-machine communication, face recognition, visual surveillance,
intelligent multimedia information processing methods, video indexing, gesture
recognition, broadcast data processing. [1, 4, 5, 7, 6, 22, 26, 27, 28]
FCTUC
people tracking, face recognition, human-machine communication, motion
detection, intentional content and expressiveness of a human body
movement using Laban analysis, Bayesian framework and human movementtracking system. [23, 25, 24]
PROBAYES
advanced probabilistic techniques, Bayesian analysis of Markov processbased models, algorithms / software for Bayesian reasoning and learning,
commercial libraries, model scenarios for risk assessments [9, 11, 10, 12]
MARAC
System Integration, Industrial activities in communications, Navigation systems,
Land Radio-communications, Telecommunications Systems, Telephone
Exchanges – PABXs, Networks, Satellite Communications Terminals, Geological/
Geophysical/ Meteorological Systems, Educational Training Systems and
Scientific Instruments
TEIC
acoustic surveillance, one-channel audio source separation, speaker
localization and tracking, Bayesian statistics and tracking [18, 21, 20, 19, 14]
Mobile Robotics Laboratory
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Partner
MM
Expected Role in the Project
FOI
2
Data fusion, surveillance systems, people tracking, assessment of
different types of threats, potential of new sensors .
UOP
11
speech recognition, microphone arrays, speaker localization and
tracking, speaker verification and identification
TUM
15
human-machine communication, face recognition, visual
surveillance, intelligent multimedia information processing
methods, video indexing, gesture recognition, broadcast data
processing.
FCTUC
20
people tracking, face recognition, human-machine communication,
motion detection, intentional content and expressiveness of a human
body movement using Laban analysis,
PROBAYES
10
advanced probabilistic techniques, Bayesian analysis of Markov
process-based models, algorithms / software for Bayesian
reasoning and learning, commercial libraries, model scenarios for risk
assessments
MARAC
0
TEIC
3
acoustic surveillance, one-channel audio source separation, speaker
localization and tracking, Bayesian statistics and tracking
Mobile Robotics Laboratory
Institute of Systems and Robotics
ISR – Coimbra
References
Mobile Robotics Laboratory
Institute of Systems and Robotics
ISR – Coimbra
Other References
• [Badler1993] N.I. Badler, C.B. Phillips, and B.L. Webber. Simulating Humans: Computer Graphics,Animation, and Control.
Oxford Univ. Press, 1993.
• [Bregler] C. Bregler. Learning and recognizing human dynamics in video sequences San Juan, Puerto Rico, 1997.
Conference on Computer Vision and Pattern Recognition.
• [Dawson] Mark Ruane Dawson. Gait recognition. Technical report, Department of Computing Imperial College of Science,
Technology & Medicine London, June 2002.
• [Kadir et al.] Timor Kadir, Richard Bowden, Eng-Jon Ong, and Andrew Zisserman. Minimal training, large lexicon,
unconstrained sign language recognition. In British Machine Vision Conference 2004. British Machine Vision Conference
2004, 2004. Winner of the Industrial Paper Prize.
• [Mitra and Acharya] Sushmita Mitra and Tinku Acharya. Gesture recognition: A survey. IEEE TRANSACTIONS ON SYSTEMS,
MAN AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS, 37, MAY 2007.
• [Bartlett] Bartlett M.S., Littlewort G., Fasel I., and Movellan J.R. Real time face detection and expression recognition:
Development and application to human-computer interaction. CVPR Workshop on Computer Vision and Pattern
Recognition for Human-Computer Interaction., 2003.
• [Nakata] T. Nakata, T. Mori, and T. Sato. Analysis of impression of robot bodily expression. Journal of Robotics and
Mechatronics, 14:2736, 2002.
• [C. Rao et al.] C. Rao, A. Yilmaz, and M. Shah. View-invariant representation and recognition of actions. Internation Journal
of Computer Vision, pages 203226, 2002.
• [Rett&Dias2007] J. Rett and J. Dias. Human robot interaction based on bayesian analysis of human movements.
Proceedings of EPIA 07, Lecture Notes in AI, Springer Verlag, Berlin., 2007.
• [Rett&Dias2008] Joerg Rett, Jorge Dias, and Juan-Manuel Ahuactzin. Laban Movement Analysis using a Bayesian model
and perspective projections. Brain, Vision and AI, 2008. ISBN: 978-953-7619-04-6.
• [Starner&Pentland] T. Starner and A. Pentland. Visual recognition of american sign language using hidden markov models,.
pages 189194, Zurich, Switzerland, 1995. International Workshop on Automatic Face and Gesture Recognition.
• [Wang et al] Liang Wang, Weiming Hu, and Tieniu Tan. Recent developments in human motion analysis. Pattern Recognition
Society, pages 585601, 2003.
• [Zhao&Badler] L. Zhao and Badler, N.I. Acquiring and validating motion qualities from live limb gestures. Graphical Models
67, pages 116, 2005.
Mobile Robotics Laboratory
Institute of Systems and Robotics
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Thank you for your attention!
Mobile Robotics Laboratory
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