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Parametric Primitives for Hand Gesture Recognition

Authors: Sanmohan Krüger, Volker Krüger

Abstract:

Imitation learning is considered to be an effective way of teaching humanoid robots and action recognition is the key step to imitation learning. In this paper an online algorithm to recognize parametric actions with object context is presented. Objects are key instruments in understanding an action when there is uncertainty. Ambiguities arising in similar actions can be resolved with objectn context. We classify actions according to the changes they make to the object space. Actions that produce the same state change in the object movement space are classified to belong to the same class. This allow us to define several classes of actions where members of each class are connected with a semantic interpretation.

Keywords: Parametric actions, Action primitives, Hand gesture recognition, Imitation learning

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080026

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References:


[1] M. A. Lewis and G. Bekey, Automation and robotics in neurosurgery: Prospects and problems, M. L. Apuzzo, Ed. AANS Publications, 1992.
[2] G. Ballantyne, "Robotic surgery, telerobotic surgery, telepresence, and telementoring," Surgical Endoscopy, vol. 16, pp. 1389-1402, 2002.
[3] B. Jiang, A. Sample, R. Wistort, and A. Mamishev, "Autonomous robotic monitoring of underground cable systems," in Advanced Robotics, 2005. ICAR -05. Proceedings., 12th International Conference on, July 2005,pp. 673-679.
[4] N. Ishikawa and K. Suzuki, "Development of a human and robot collaborative system for inspecting patrol of nuclear power plants," in Robot and Human Communication, 1997. RO-MAN -97. Proceedings., 6th IEEE International Workshop on, Sep-1 Oct 1997, pp. 118-123.
[5] W. Song, H. Lee, J. Kim, Y. S. Yoon, and K. Bien, "Intelligent rehabilitation robotics system for the disabled and the elderly," in Proc. of IEEE 20th Annual International Conference on Engineering in Medicine and Biology Society, vol. 5, 1998, pp. 2682-2685.
[6] K. Kawamura, S. Bagchi, M. Iskarous, and M. Bishay, "Intelligent robotic systems in service of the disabled," Rehabilitation Engineering, IEEE Transactions on, vol. 3, no. 1, pp. 14-21, Mar 1995.
[7] V. Gallese, L. Fadiga, L. Fogassi, and G. Rizzolatti, "Action recognition in the premotor cortex," Brain, vol. 119, no. 2, pp. 593-609, 1996.
[8] G. Rizzolatti, L. Fadiga, V. Gallese, and L. Fogassi, "Premotor cortex and the recognition of motor actions," Cognitive Brain Research, vol. 3, no. 2, pp. 131-141, March 1996.
[Online]. Available: http://dx.doi.org/10.1016/0926-6410(95)00038-0
[9] I. S. Vicente, V. Kyrki, D. Kragic, and M. Larsson, "Action recognition and understanding through motor primitives," Advanced Robotics, vol. 21, no. 15, pp. 1687-1707, 2007.
[10] A. Fod, M. J. Matari'c, and O. C. Jenkins, "Automated derivation of primitives for movement classification," Autonomous Robots, vol. 12, no. 1, pp. 39-54, 2002.
[Online]. Available: http://www.springerlink. com/content/n14t2272246jj54p
[11] C. F. Gutemberg Guerra-Filho and Y. Aloimonos, "Discovering a language for human activity," in AAAI 2005 Fall Symposium on Anticipatory Cognitive Embodied Systems, Washington, D.C, 2005, pp. 70-77.
[12] G. Guerra-Filho and Y. Aloimonos, "A language for human action," Computer, vol. 40, no. 5, pp. 42-51, 2007.
[13] A. Kojima, T. Tamura, and K. Fukunaga, "Natural language description of human activities from video images based on concept hierarchy of actions," Int. J. Comput. Vision, vol. 50, no. 2, pp. 171-184, 2002.
[14] O. C. Jenkins, M. J. Mataric, and S. Weber, "Primitive-based movement classification for humanoid imitation," 2000.
[15] M. J. Matari'c, B. Zordan Victor, and M. M. Williamson, "Making complex articulated agents dance," Autonomous Agents and Multi-Agent Systems, vol. 2, no. 1, pp. 23-43, 1999.
[16] Sanmohan and V. Krueger, Primitive Based Action Representation and Recognition, ser. Image Analysis, R. J. A.B Salberg, J.Y. Hardeberg, Ed. Springer Berlin / Heidelberg, 2009, vol. LNCS 5575.
[Online]. Available: http://www.springerlink.com/content/yv5501516404t5h8/?p= d1f1d06139ae4f05ba5d6701fd9a4e2c&pi=3
[17] A. D. Wilson and A. F. Bobick, "Parametric hidden markov models for gesture recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 884-900, 1999.
[18] A. Bobick, "Movement, Activity, and Action: The Role of Knowledge in the Perception of Motion," Philosophical Trans. Royal Soc. London, vol. 352, pp. 1257-1265, 1997.
[19] C. Stauffer and W. Grimson, "Learning Patterns of Activity Using Real- Time Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, 2000.
[20] N. Robertson and I. Reid, "Behaviour Understanding in Video: A Combined Method," in Internatinal Conference on Computer Vision, Beijing, China, Oct 15-21, 2005.
[21] K. Nelissen, G. Luppino, W. Vanduffel, G. Rizzolatti, and G. A. Orban, "Observing Others: Multiple Action Representation in the Frontal Lobe," Science, vol. 310, no. 5746, pp. 332-336, 2005.
[Online]. Available: http://www.sciencemag.org/cgi/content/abstract/310/5746/332
[22] D. Bub N and Michael E. J. Masson, "Gestural knowledge evoked by objects as part of conceptual representations," Aphasiology, vol. 20, no. 9-11, pp. 1112-1124, November 2006.
[Online]. Available: http://dx.doi.org/10.1080/02687030600741667
[23] S. Ullman, "High-level vision: Object recognition and visual cognition," July 1996.
[24] D. Moore, I. Essa, and I. Hayes, M.H., "Exploiting human actions and object context for recognition tasks," vol. 1, pp. 80-86 vol.1, 1999.
[25] A. Gupta and L. Davis, "Objects in action: An approach for combining action understanding and object perception," pp. 1-8, June 2007.
[26] I. S. Vicente, V. Kyrki, and D. Kragic, "Action recognition and understanding through motor primitives," Advanced Robotics, vol. 21, pp. 1687-1707, 2007.
[27] L. R. Rabiner, "A tutorial on hidden markov models and selected applications inspeech recognition," Procceeding of the IEEE, vol. 77, no. 2, pp. 257-286, 1989.
[28] Vladimir Dragalin, V. Fedorov, S. Patterson, and B. Jones., "Kullbackleibler divergence for evaluating bioequivalence," Statistics in Medicine, vol. 22, no. 6, pp. 913-930, 2003.
[29] D. S. Hirschberg, "Algorithms for the longest common subsequence problem," J. ACM, vol. 24, no. 4, pp. 664-675, 1977.