JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Compensation Algorithm for the Position of User Hands Based on Moving Mean-Shift for Gesture Recognition in HRI System
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Compensation Algorithm for the Position of User Hands Based on Moving Mean-Shift for Gesture Recognition in HRI System
Kim, Tae-Wan; Kwon, Soon-Ryang; Lee, Dong Myung;
  PDF(new window)
 Abstract
A Compensation Algorithm for The Position of the User Hands based on the Moving Mean-Shift () in Human Robot Interface (HRI) System running the Kinect sensor is proposed in order to improve the performance of the gesture recognition is proposed in this paper. The average error improvement ratio of the trajectories () in left-right movements of hands for the is compared with other compensation algorithms such as the Compensation Algorithm based on the Compensation Algorithm based on the Kalman Filter () and the Compensation Algorithm based on Least-Squares Method () by the developed realtime performance simulator. As a result, the in up-down movements of hands of the is measured as 19.35%, it is higher value compared with that of the and the as 13.88% and 16.68%, respectively.
 Keywords
Kinect;Hands Gesture;Gesture Recognition;Compensation;Human Robot Interface;
 Language
Korean
 Cited by
 References
1.
S. C. Kim and I. C. Park, "A study on the ubiquitous home network interface system by application of user's gesture recognition method," J. Korean of The Science of Emotion & Sensibility (KJSOS), vol 8. no 3, pp. 265-276, Sep. 2005.

2.
F. K. H. Quek, "Unencumbered gestural interaction," IEEE Multimedia, vol. 3, pp. 36-47, Dec. 1996. crossref(new window)

3.
S. Mitra and T. Acharya, "Gesture recognition: A survey," IEEE Trans. Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 37, no. 3, pp. 311-324, 2007.

4.
G.R.S. Murthy and R.S. Jadon, "A review of vision based hand gestures recognition," J. Int'l Information Technol. and Knowledge Management (IJITKM), vol. 2, no. 2, pp. 405-410, July-December 2009.

5.
H. J. Kim, Kinect for windows 1: Implement of the innovative user interfaces of embedded devices (2012), Retrieved Feb. 1, 2015, from http://www.mdstec.com/bbs/press_room/upload/Kinect for Windows_4.pdf

6.
P. Doliotis, et al., "Comparing gesture recognition accuracy using color and depth information," in Proc. 4th Int. Conf. Pervasive Technol. Related to Assistive Environments (PETRA'11), no. 20, pp. 1-7, USA, May 2011.

7.
M. Yang, N. Ahuja, and M. Tabb, "Extraction of 2D motion trajectories and its application to hand gesture recognition," IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 8, pp. 1061-1074, Aug. 2002. crossref(new window)

8.
E. J. Holden, G. Lee, and R. Owens, "Australian sign language recognition," J. Machine Vision and Appl., vol. 16, no. 5, pp. 312-320, 2005. crossref(new window)

9.
S. Y. Cho, et al., "Hand gesture recognition from kinect sensor data," J. Korea Broadcast Eng. (KJBE), vol. 17. no 3, pp. 447-458, May 2012. crossref(new window)

10.
A. R. Kim and S. Y. Rhee, "Motion control of a mobile robot using natural hand gesture," J. The Korean Inst. Intell. Syst. (KJIIS), vol. 24, no 1, pp. 64-70, Feb. 2014. crossref(new window)

11.
G. Welch and G. Bishop, An Introduction to the Kalman Filter (2006), http://www.cs.unc.edu/-welch/media/pdf/kalman_intro.pdf/

12.
T. W. Kim, "A study on the localization compensation algorithm using Mean kShift/ Kalman Filter in random walk/waypoint mobility model environment," Master's Thesis, The Graduate School of Tongmyong University, Feb. 2014.

13.
Konstantinos G. Derpanis, Mean shift clustering, Computer Vision Notes (2005), http://www.cse.yorku.ca/-kosta/CompVis_Notes/mean_shift.pdf/

14.
M. K. Jung and D. M. Lee, "Performance analysis of the localization compensation algorithm for moving objects using the least-squares method," J. KICS, vol. 39C, no. 1, pp. 9-16, Jan. 2014.