Trajectory Guided Recognition of Hand Gestures having only Global Motions

One very interesting field of research in Pattern Recognition that has gained much attention in recent times is Gesture Recognition. In this paper, we consider a form of dynamic hand gestures that are characterized by total movement of the hand (arm) in space. For these types of gestures, the shape of the hand (palm) during gesturing does not bear any significance. In our work, we propose a model-based method for tracking hand motion in space, thereby estimating the hand motion trajectory. We employ the dynamic time warping (DTW) algorithm for time alignment and normalization of spatio-temporal variations that exist among samples belonging to the same gesture class. During training, one template trajectory and one prototype feature vector are generated for every gesture class. Features used in our work include some static and dynamic motion trajectory features. Recognition is accomplished in two stages. In the first stage, all unlikely gesture classes are eliminated by comparing the input gesture trajectory to all the template trajectories. In the next stage, feature vector extracted from the input gesture is compared to all the class prototype feature vectors using a distance classifier. Experimental results demonstrate that our proposed trajectory estimator and classifier is suitable for Human Computer Interaction (HCI) platform.

• M. K. Bhuyan
• P. K. Bora
• D. Ghosh

• Hand gesture
• human computer interaction
• key video object plane
• dynamic time warping.

[1] V.I. Pavlovic, R. Sharma and T.S. Huang, Visual interpretation of hand
gestures for human-computer interaction: A review, IEEE Trans. Pattern
Analysis and Machine Intelligence, 19(7) (1997) 677-695.
[2] D.L. Quam, Gesture recognition with a data glove, Proc. IEEE Conf.
National Aerospace and Electronics, Vol. 2, 1990, pp. 755-760.
[3] D.J. Sturman and D. Zeltzer, A survey of glove-based input, IEEE
Computer Graphics and Applications, 14 (1994) 30-39.
[4] M.H. Yang, N. Ahuja and M. Tabb, Extraction of 2D motion trajectories
and its application to hand gesture recognition, IEEE Trans. Pattern
Analysis and Machine Intelligence, 24(8) (2002) 1061-1074.
[5] Y. Wu and T.S. Huang, Hand modelling, analysis, and recognition
for vision-based human computer interaction, IEEE Signal Processing
Magazine, (2001) 51-60.
[6] M. Black and A. Jepson, Recognition temporal trajectories using the
condensation algorithm, Proc. International Conf. Automatic Face and
Gesture Recognition, 1998, pp. 16-21.
[7] M. J. Black and A. D. Jepson, A probabilistic framework for matching
temporal trajectories: Condensation-based recognition of gestures and
expressions, Proc. European Conf. Computer Vision, Vol. 1, 1998, pp.
909-924.
[8] D.P. Huttenlocher, J.J. Noh and W.J. Rucklidge, Tracking non-rigid
objects in complex scene, Proc. 4th International Conf. Computer Vision,
1993, pp. 93-101.
[9] B.S. Manjunath, P. Salembier, T. Sikora, (Ed.), Intoduction to MPEG-7,
Multimedia Content Description Interface, (John Wiley and Sons Ltd,
New York, 2002).
[10] L. R. Rabiner and B. Juang, Fundamentals of Speech Recognition,
(Prentice Hall, Englewood Cliffs, N.J., 1993).
[11] G. Borgefors, Distance transformations in digital images, Computer
Vision, Graphics and Image Processing, 34 (1986) 344-371.
[12] A. K. Jain, Fundamentals of Digital Image Processing, (Prentice-Hall,
Englewood Cliffs, NJ, 1989).
[13] M.K. Bhuyan, D. Ghosh and P.K. Bora, Finite state representation of
hand gestures using key video object plane, Proc. IEEE Region 10 -
Asia-Pacific Conf. TENCON, 2004, pp. 21-24.
[14] H. Sakoe, S. Chiba, Dynamic programming algorithm optimization for
spoken word recognition, IEEE Trans. on Acoustics, Speech and Signal
Processing. 26 (1) (1978) 43-49.
[15] I.C. Kim and S.I. Chien, Analysis of 3D hand trajectory gestures using
stroke-based composite Hidden Markov Models, Applied Intelligence, 15
(2001) 131-143.