Abstract
Gestures are expressive and meaningful body motions used in daily life as a means of communication so many researchers have aimed to provide natural ways for human-computer interaction through automatic gesture recognition. However, most of researches on recognition of actions focused mainly on sign gesture. It is difficult to directly extend to recognize whole body gesture. Moreover, previous approaches used manually segmented image sequences. This paper focuses on recognition and segmentation of whole body gestures, such as walking, running, and sitting. We introduce the gesture spotting algorithm that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture pattern. In the proposed gesture spotting algorithm, the likelihood of non-gesture Hidden Markov Models(HMM) can be used as an adaptive threshold for selecting proper gestures. The proposed method has been tested with a 3D motion capture data, which are generated with gesture eigen vector and Gaussian random variables for adequate variation. It achieves an average recognition rate of 98.3% with six consecutive gestures which contains non-gestures.
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
Wilson, A.D., Bobic, A.F.: Parametric Hidden Markov Models for Gesture Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(9), 884–900 (1999)
Vaananen, K., Boehm, K.: Gesture Driven Interaction as a Human Factor in Virtual Environments – An Approach with Neural Networks. Virtual Reality Systems, 93–106 (1993)
Starner, T., Weaver, J., Pentland, A.: Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(12), 1371–1375 (1998)
Quek, F.: Toward a Vision-Based Hand Gesture Interface. In: Proc. of Virtual Reality System Technology Conf., Singapore, pp. 17–29 (1994)
Kahol, K., Tripath, P., Panchanthan, S.: Automated Gesture Recognition From Dance Sequences. In: Proc. of Int’l Conf. on Automatic Face and Gesture Recognition, Seoul, pp. 883–888 (2004)
Yang, H.-D., Park, S.-K., Lee, S.-W.: Reconstruction of 3D Human Body Pose Based on Top-down Learning
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley & Sons, New York (2001); Proc. of Int’l Conf. on Intelligent Computing, Hefei (to appear, 2005)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. of IEEE 77, 257–286 (1989)
Viterbi, A.J.: Error Bounds for Convolution Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Trans. on Information Theory 13, 260–269 (1967)
Hwang, B.-W., Kim, S.-M., Lee, S.-W.: 2D and 3D Full-Body Gesture Database for Analyzing Daily Human Gestures. In: Proc. of Int’l Conf. on Intelligent Computing, Hefei (to appear, 2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, AY., Lee, SW. (2006). Gesture Spotting in Continuous Whole Body Action Sequences Using Discrete Hidden Markov Models. In: Gibet, S., Courty, N., Kamp, JF. (eds) Gesture in Human-Computer Interaction and Simulation. GW 2005. Lecture Notes in Computer Science(), vol 3881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11678816_12
Download citation
DOI: https://doi.org/10.1007/11678816_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32624-3
Online ISBN: 978-3-540-32625-0
eBook Packages: Computer ScienceComputer Science (R0)