Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-05T15:21:54.233Z Has data issue: false hasContentIssue false

Tracking and replication of hand movements by teleguided intelligent manipulator robot

Published online by Cambridge University Press:  11 February 2014

A. T. Hussain*
Affiliation:
Autonomous & Machine Vision Research Cluster, Universiti Malaysia Perlis (UniMAP), Malaysia
S. Faiz Ahmed
Affiliation:
Autonomous & Machine Vision Research Cluster, Universiti Malaysia Perlis (UniMAP), Malaysia
D. Hazry
Affiliation:
Autonomous & Machine Vision Research Cluster, Universiti Malaysia Perlis (UniMAP), Malaysia
*
*Corresponding author. E-mail: asthussain@yahoo.com

Summary

In this paper, a new method is presented that allows an intelligent manipulator robotic system to track a human hand from far distance in 3D space and estimate its orientation and position in real time with the goal of ultimately using the algorithm with a robotic spherical wrist system. In this proposed algorithm, several image processing and morphology techniques are used in conjunction with various mathematical formulas to calculate the hand position and orientation. The proposed technique was tested on Remote teleguided virtual Robotic system. Experimental results show that proposed method is a robust technique in terms of the required processing time of estimation of orientation and position of hand.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Pavlovic, V. I., Sharma, R. and Huang, T. S., “Visual interpretation of hand gesturesfor human-computer interaction: A review,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 677695 (1997).CrossRefGoogle Scholar
2.Maung, T. H. H., “Real-Time hand tracking and gesturerecognition system using neural networks,” World Acad. Sci., Eng. Technol. 38, 470474 (2009).Google Scholar
3.Manresa, C., Varona, J., Mas, R. and Perales, F. J., “Hand tracking and gesture recognition forhuman-computer interaction,” Electron. Lett. Comput. Vis. Andimage Anal. 5, 96104 (2005).Google Scholar
4.Elmezain, M., Al-Hamadi, A., Niese, R. and Michaelis, B., “A robust method for hand tracking using mean-shift algorithm and kalman filter in stereo colour image sequences,” World Acad. Sci. Eng. Technol. (35), 283287 (Nov. 2009).Google Scholar
5.Cui, J. S. and Su, Z. Q., “Vision-Based Hand Motion Capture Using Genetic Algorithm,” Lecture Notes in Computer Science - Applications of Evolutionary Computing, vol. 3005, pp. 289300, 2004.Google Scholar
6.Binh, N. D., Shuichi, E. and Ejima, T., “Real-Time Hand Tracking and Gesture Recognition System,” Proceedings of International Conference on Graphics, Vision and Image (Dec. 19–21, 2005) pp. 362368.Google Scholar
7.Lee, H. K. and Kim, J. H., “An HMM-Based threshold model approach for gesture recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 21 (10), 961973 (Oct. 1999).Google Scholar
8.Starner, T. and Pentland, A., “Real-Time American Sign Language Recognition from Video Using Hidden Markov Models,” Technical Report 375, MIT Media Lab, Perceptual Computing Group (1995).Google Scholar
9.Kjeldsen, R. and Kender, J., “Visual Hand Gesture Recognition for Window System Control,” Proceedings of Int. Workshop on Automatic Face and Gesture Recognition, Zurich, Switzerland (1995) pp. 184188.Google Scholar
10.Zhao, M., Quek, F. K. H. and Wu, X., “RIEVL: Recursive induction learning in hand gesture recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 20 (11), 11741185 (Nov. 1998).Google Scholar
11.Yoon, H., Soh, J., Baeand, Y. J. and Yang, H. S., “Hand gesture recognition using combined features of location, angle and velocity,” Pattern Recognit. 34 (7), 14911501 (2001).Google Scholar
12.Lockton, R. and Fitzgibbon, A. W., “Real-Time Gesture Recognition Using Deterministic Boosting,” Proceedings of the British Machine Vision Conference (2002) pp. 817–826.Google Scholar
13.Bowden, R., Windridge, D., Kadir, T., Zisserman, A. and Brady, M., “A linguistic Feature Vector for the Visual Interpretation of Sign Language,” Proceedings of the European Conference on Computer Vision (2004) pp. 390–401.Google Scholar
14.Jeon, M., Wan Lee, S. and Bien, Z., “Hand gesture recognition using multivariate Fuzzy Decision Tree and user adaptation,” Int. J. Fuzzy Syst. Appl. 1 (3), 1531 (2011).Google Scholar
15.Caridakis, G., Karpouzis, K., Drosopoulos, A. and Kollias, S., “SOMM: Self organizing Markov map for gesture recognition,” Pattern Recognit. Lett. 31 (1), 5259 (2010).Google Scholar
16.Suk, H., Sin, B. and Lee, S., “Hand gesture recognition based on dynamic Bayesian network framework,” Pattern Recognit. 43 (9), 30593072 (Sep. 2010) Department of Brain and Cognitive Engineering, Korea University.Google Scholar
17.Tomasi, C., Petrov, S. and Sastry, A., “3D Tracking = Classification + Interpolation,” Proceedings of the IEEE International Conference on Computer Vision, 2, (Oct. 2003) pp. 14411448.Google Scholar
18.Lam, L., Lee, S. W. and Suen, C. Y., “Thinning methodologies- A comprehensive survey,” IEEE Trans. Pattern Anal. Mach. Intell. 14 (9), 869885 (Sep. 1992).Google Scholar