Abstract
This paper proposes a method to recognize human gestures from an image sequence based on Hidden Markov Model and Karhunen-Lo`eve Transform. As our method uses the motion vector field of the scene for recognition, it is robust for variety in the background of the scene and it doesn't require the users to wear a sensor or a marker. The motion vector field of the scene is projected to an eigen-subspace for data compression and is used as the input symbols for the HMM. View-based methods generally fail when the user translates. Our method is robust for recognition to the user's translation because the recognition window automatically fits the user by tracking the user's face.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Katsuhiko Takahashi, Susumu Seki, and Ryuichi Oka. Spotting recognition of human gestures from motion images. PRU 92-157, IEICE, Mar 1993.
Junji Yamato, Shoji Kurakake, Akira Tomono, and Kenichiro Ishii. Human action recognition using HMM with category-separated vector quantization. IEICE, J77D-II(7):1311–1318, Jul 1994.
Lee W. Campbell, David A. Becker, Ali Azarbayejani, Aaron F. Bobick, and Alex Pentland. Invariant features for 3D gesture recognition. In International Conference on Automatic Face and Gesture Recognition, pp. 157–162, Killington, Oct 1996.
Takahiro Watanabe, Akitoshi Tsukamoto, Chil-Woo Lee, and Masahiko Yachida. Gesture recognition using multiple silhouette models for a real-time interactive system. In Asian Conference on Computer Vision, Vol. 2, pp. 235–239, 1995.
Shigeki Nagaya, Susumu Seki, and Ryuichi Oka. A theoretical consideration of pattern space trajectory-for gesture spotting recognition. In International Conference on Automatic Face and Gesture Recognition, pp. 72–77, Oct 1996.
Haiyuan Wu, Qian Chen, and Masahiko Yachida. A fuzzy theory based face detector. In International Conference on Pattern Recognition, Vol. 3, pp. 406–410, 1996.
Errki Oja. Subspace Methods of Pattern Recognition. Research Studies Press, 1983.
Hiroshi Murase and Shree K. Nayar. 3d object recognition from appearance — parametric eigenspace method. IEICE, J77-D-II(11):2179–2187, Nov 1994.
Lawrence R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE., 77(2):257–285, 1989.
G. Rigoll, A. Kosmala, J. Rottland, and C. Neukirchen. A comparison between continuous and discrete density hidden markov models for cursive handwriting recognition. Vol. 2, pp. 205–209, 1996.
Hiroshi Murase and Shree K. Nayar. Illumination planning for object recognition in structure environments. pp. 31–38, 1994.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iwai, Y., Rata, T., Yachida, M. (1997). Gesture recognition from image motion based on subspace method and HMM. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63931-4_272
Download citation
DOI: https://doi.org/10.1007/3-540-63931-4_272
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63931-2
Online ISBN: 978-3-540-69670-4
eBook Packages: Springer Book Archive