Estimation of pointing poses for visually instructing mobile robots under real world conditions

Robotics and Autonomous Systems

Volumn 58, Issue 2, 2010, Pages 174-185

  Martin, Christian ^a,b   Steege, Frank Florian ^a   Gross, Horst Michael ^a  

^a ILMENAU UNIVERSITY OF TECHNOLOGY   (Germany)

^b MetraLabs Neue Technologien und Systeme GmbH   (Germany)

Author keywords

Human Robot interaction; Pose recognition; Vision

Indexed keywords

BACKGROUND SUBTRACTION TECHNIQUES; FEATURE EXTRACTION METHODS; FUNCTION APPROXIMATORS; HISTOGRAM EQUALIZATIONS; HUMAN ROBOT INTERACTIONS; INPUT FEATURES; LIGHTING CONDITIONS; MOBILE INTERACTION; MONOCULAR VISION; PREPROCESSING TECHNIQUES; PROCESSING CHAIN; PROCESSING STEPS; REAL-WORLD; REAL-WORLD SCENARIO; STRUCTURED BACKGROUND; TARGET POSITION; TRAINING DATA;

DISCRIMINANT ANALYSIS; ESTIMATION; FEATURE EXTRACTION; HUMAN COMPUTER INTERACTION; MAN MACHINE SYSTEMS; MOBILE ROBOTS;

HUMAN ROBOT INTERACTION;

EID: 75149153253     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2009.09.013     Document Type: Article

References (26)

1. H.-M. Gross, J. Richarz, S. Müller, A. Scheidig, C. Martin, Probabilistic multi-modal people tracker and monocular pointing pose estimator for visual instruction of mobile robot assistants, in: Proc. of the IEEE World Congress on Computational Intelligence, WCCI, 2006, pp. 8325-8333
2. Richarz J., Scheidig A., Martin C., Müller S., and Gross H.-M. A monocular pointing pose estimator for gestural instruction of a mobile robot. International Journal of Advanced Robotic Systems 4 1 (2007) 139-150
3. O. Rogalla, M. Ehrenmann, R. Zöllner, R. Becher, R. Dillmann, Using gesture and speech control for commanding a robot assistant, in: Proc. of the 11th IEEE Int. Workshop on Robot and Human Interactive Communication, 2002, ROMAN, 2002, pp. 454-459
4. Paquin V., and Cohen P. A vision-based gestural guidance interface for mobile robotic platforms. Proc. of the Workshop on HCI, Computer Vision in Human-Computer Interaction. ECCV. LNCS vol. 3058 (2004), Springer 39-47
5. Triesch J., and von der Malsburg C. A system for person-independent hand posture recognition against complex backgrounds. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 12 (2001) 1449-1453
6. Hofemann N., Fritsch J., and Sagerer G. Recognition of deictic gestures with context. Proceedings of the 26th DAGM Symposium on Pattern Recognition vol. 3175 (2004), Springer 334-341
7. M. Bennewitz, T. Axenbeck, S. Behnke, W. Burgard, Robust recognition of complex gestures for natural human-robot interaction, in: Proc. of the Workshop on Interactive Robot Learning at Robotics: Science and Systems Conference, RSS, 2008
8. Li Z., Hofemann N., Fritsch J., and Sagerer G. Hierarchical modeling and recognition of manipulative gesture. Proceedings of the IEEE International Conference on Computer Vision, Workshop on Modeling People and Human Interaction (2005), IEEE Computer Society
9. Nickel K., and Stiefelhagen R. Visual recognition of pointing gestures for human-robot interaction. Image and Vision Computing 25 12 (2007) 1875-1884
10. Wilson A.D., and Bobick A.F. Parametric hidden Markov models for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (1999) 884-900
11. Hosoya E., Sato H., Kitabata M., Harada I., Nojima H., and Onozawa A. Arm-pointer: 3D pointing interface for real-world interaction. ECCV Workshop on HCI. Lecture Notes in Computer Science vol. 3058 (2004), Springer
12. N. Jojic, B. Brumitt, B. Meyers, S. Harris, T. Huang, Detection and estimation of pointing gestures in dense disparity maps, in: The fourth International Conference on Automatic Face- and Gesture-Recognition, 2000, pp. 468-475
13. Hung Y., Yang Y., Chen Y., Hsieh I., and Fuh C. Free-hand pointer by use of an active stereo vision system. ICPR'98: Proceedings of the 14th International Conference on Pattern Recognition vol. 2 (1998) 1244-1246
14. C. Nölker, H. Ritter, Illumination independent recognition of deictic arm postures, in: Proc. of the 24th Annual Conference of the IEEE Industrial Electronics Society, 1998, pp. 2006-2011
15. Urano T., Matsui T., Nakata T., and Mizoguchi H. Human pose recognition by memory-based hierarchical feature matching. SMC (7) (2004), IEEE 6412-6416
16. Takahashi K., and Sugakawa S. Remarks on human posture classification using self-organizing map. SMC (3) (2004), IEEE 2623-2628
17. R. Stiefelhagen, Estimating head pose with neural networks-Results on the pointing04 ICPR workshop evaluation data, in: Proc. of the Pointing 04 ICPR Workshop, 2004
18. Krüger V., and Sommer G. Gabor wavelet networks for efficient head pose estimation. Image and Vision Computing 20 9-10 (2002) 665-672
19. Martin C., Scheidig A., Wilhelm T., Schröter C., Böhme H.-J., and Gross H.-M. A new control architecture for mobile interaction robots. Proc. of the 2nd European Conference on Mobile Robots. ECMR 2005 (2005), Stampalibri 224-229
20. P.A. Viola, M.J. Jones, Rapid object detection using a boosted cascade of simple features, in: Proc. of the Conf. on Computer Vision and Patter Recognition, 2001, pp. 511-518
21. Horn B.K.P. Robot Vision (1986), MIT press
22. Lachenbruch P.A. Discriminant Analysis (1975), Hafner
23. McLachlan G.J. Discriminant Analysis and Statistical Pattern Recognition (2004), Wiley-Interscience
24. T. Martinetz, K. Schulten, A Neural-Gas network learns topologies, in: Proc. of the ICANN 1991, 1991, pp. 397-402
25. Kohonen T. Self-organized formation of topologically correct feature maps. Biological Cybernetics 43 (1982) 59-69
26. Ritter H. In: Kohonen T., et al. (Ed). Learning with the Self-Organizing Map. Artifical Neural Networks (1991), Elsevier Science