a small volume above the panel with the icon sitting at thecenter of the bottom. The height and diameter of the volume
is also limited to be able to capture enough visual cuesto carry out successful gesture recognition.The remainder of this paper is structured as follows. InSection 2 we present a novel method to efficiently capturethe 3D spatial information of the gesture without carrying
out a full-scale disparity computation. We discuss howProceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’04)
1063-6919/04 $ 20.00 IEEEAuthorized licensed use limited to: MACQUARIE UNIV. Downloaded on July 5, 2009 at 22:40 from IEEE Xplore. Restrictions apply.
to learn the cluster structure of the appearance and motion
features via an unsupervised learning process in Section
3. Two ways to model the dynamics of the gestures,
i.e., forward HMMs [10, 19] and multilayer neural networks[6], are also presented. In Section 4 we demonstrateour real-time system that implements the proposed methodand present the results of gesture recognition.
1.1 RelatedWor
References
[1] Vassilis Athitsos and Stan Sclaroff. Estimating 3D Hand
Pose from a Cluttered Image. In Computer Vision and Pattern
Recognition, volume 2, pages 432–439, 2003.
[2] Aaron Bobick and Andrew Wilson. A State-based Approach
to the Representation and Recognition of Gesture. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
19(12):1325–1337, 1997.
[3] Jason J. Corso, Darius Burschka, and Gregory D. Hager. The
4DT: Unencumbered HCI With VICs. In Proceedings of
CVPRHCI, 2003.
[4] James Davis and Aaron Bobick. The Representation and
Recognition of Action Using Temporal Templates. In Computer
Vision and Pattern Recognition, pages 928–934, 1997.
[5] Jonathan Deutscher, Andrew Blake, and Ian Reid. Articulated
Body Motion Capture by Annealed Particle Filtering.
Computer Vision and Pattern Recognition, 2, 2000.
[6] Richard Duda, Peter Hart, and David Stork. Pattern Classification.
John Wiley and Sons, Inc, 2001.
[7] Graham D. Finlayson, James Crowley, and Bernt Schiele.
Comprehensive Colour Image Normalization. In Proceedings
of the European Conference on Computer Vision, number
1, pages 475–490, 1998.
[8] D. Gavrila. The visual analysis of human movement: a survey.
Computer Vision and Image Understanding, 73:82–98,
1999.
[9] Theo Gevers. Color based object recognition. Pattern Recognition,
32(3):453–464, 1999.
[10] Frederick Jelinek. Statistical Methods for Speech Recognition.
MIT Press, 1999.
[11] S. Malassiotis, N. Aifanti, and M. Strintzis. A Gesture
Recognition System Using 3D Data. In Proceedings of the
First International Symposium on 3D Data Processing Visualization
and Transmisssion, pages 190–193, 2002.
[12] Kenji Oka, Yoichi Sato, and Hideki Koike. Real-Time Fingertip
Tracking and Gesture Recognition. IEEE Computer
Graphics and Applications, 22(6):64–71, 2002.
[13] Vasu Parameswaran and Rama Chellappa. View Invariants
for Human Action Recognition. In Computer Vision and Pattern
Recognition, volume 2, pages 613–619, 2003.
[14] F. Quek. Unencumbered Gesture Interaction. IEEE Multimedia,
3(3):36–47, 1996.
[15] Lawrence Rabiner. A Tutorial on Hidden Markov Models
and Selected Applications in Speech Recognition. Proceedings
of the IEEE, 77(2):257–286, 1989.
[16] Aditya Ramamoorthy, Namrata Vaswani, Santanu Chaudhury,
and Subhashi Banerjee. Recognition of Dynamic Hand
Gestures. Pattern Recognition, 36:2069–2081, 2003.
[17]
