Abstract
We are interested in methods for building cognitive vision systems to understand activities of expert operators for our ActIPret System. Our approach to the gesture recognition required here is to learn the generic models and develop methods for contextual bias of the visual interpretation in the online system. The paper first introduces issues in the development of such flexible and robust gesture learning and recognition, with a brief discussion of related research. Second, the computational model for the Hidden Markov Model (HMM) is described and results with varying amounts of noise in the training and testing phases are given. Third, extensions of this work to allow both top-down bias in the contextual processing and bottom-up augmentation by moment to moment observation of the hand trajectory are described.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bauer, B., Heinz, H., Kraiss, K.: Video-based continuous sign language recognition using statistical methods. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 440–445, Grenoble, France (2000)
Black, M.J., Jepson, A.: A probabilistic framework for matching temporal. In: European Conference on Computer Vision, Cambridge, UK, pp. 343–356 (1996)
Blei, D.M., Moreno, P.J.: Topic segmentation with an aspect hidden Markov model. In: Proceedings of the 24th International Conference on Research and Development in Information Retrieval (SIGIR 2001), New York, pp. 343–348 (2001)
Bobick, A., Davies, J.: The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 257–267 (2001)
Buxton, H., Gong, S.: Visual surveillance in a dynamic and uncertain world. Artificial Intelligence 78, 431–459 (1995)
Buxton, H., Howell, A.J., Sage, K.: The role of task control and context in learning to recognise gesture. In: Cognitive Vision Workshop, Zürich, Switzerland (2002)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from imcomplete data using the em algorithm. Journal of the Royal Statistical Society 39, 185–197 (1977)
Gong, S., Buxton, H.: On the visual expectations of moving objects: A probabilistic approach with augmented hidden Markov model. In: European Conference on Artificial Intelligence, Vienna, Austria, pp. 781–785 (1992)
Howarth, R., Buxton, H.: Conceptual descriptions from monitoring and watching image sequences. Image and Vision Computing 18, 105–135 (2000)
Howell, A.J., Buxton, H.: Learning gestures for visually mediated interaction. In: British Machine Vision Conference, Southampton, UK, pp. 508–517 (1998)
Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 343–356. Springer, Heidelberg (1996)
Johansson, G.: Visual perception of biological motion and a model for its analysis. Perception and Psychophysics 14, 201–211 (1973)
McKenna, S., Gong, S.: Gesture recognition for visually mediated interaction using probabilistic event trajectories. British Machine Vision Association, Southampton, UK (1998)
Moore, D.J., Essa, I.A., Hayes, M.H.: Exploiting human actions and object context for recognition tasks. In: IEEE International Conference on Computer Vision, Vancouver, Canada, pp. 80–86 (1999)
Rabiner, L.R.: A tutorial on hidden Markov models. Proceedings of the IEEE 77, 257–286 (1989)
Schlenzig, J., Hunter, E., Jain, R.: Recursive indentification of gesture using Hidden Markov Model. In: Workshop on Applications of Computer Vision, Sarasota, Florida (1994)
Sherrah, J., Gong, S.: Resolving visual uncertainty and occlusion through probabilistic reasoning. British Machine Vision Association, Bristol, UK (2000)
Starner, T., Pentland, A.: Real-time american sign language recognition using hidden Markov models. In: International Symposium on Computer Vision, Coral Gables, FL , pp. 265–270 (1995)
Walter, M., Psarrou, A., Gong, S.: Data driven model acquistion using minimum description length. British Machine Vision Association, Manchester, UK (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sage, K., Howell, A.J., Buxton, H. (2004). Developing Context Sensitive HMM Gesture Recognition. In: Camurri, A., Volpe, G. (eds) Gesture-Based Communication in Human-Computer Interaction. GW 2003. Lecture Notes in Computer Science(), vol 2915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24598-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-24598-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21072-6
Online ISBN: 978-3-540-24598-8
eBook Packages: Springer Book Archive