Browse > Article
http://dx.doi.org/10.9708/jksci.2014.19.8.035

3D Pointing for Effective Hand Mouse in Depth Image  

Joo, Sung-Il (Dept. of Global Media, Soongsil University)
Weon, Sun-Hee (Dept. of Global Media, Soongsil University)
Choi, Hyung-Il (Dept. of Global Media, Soongsil University)
Abstract
This paper proposes a 3D pointing interface that is designed for the efficient application of a hand mouse. The proposed method uses depth images to secure high-quality results even in response to changes in lighting and environmental conditions and uses the normal vector of the palm of the hand to perform 3D pointing. First, the hand region is detected and tracked using the existing conventional method; based on the information thus obtained, the region of the palm is predicted and the region of interest is obtained. Once the region of interest has been identified, this region is approximated by the plane equation and the normal vector is extracted. Next, to ensure stable control, interpolation is performed using the extracted normal vector and the intersection point is detected. For stability and efficiency, the dynamic weight using the sigmoid function is applied to the above detected intersection point, and finally, this is converted into the 2D coordinate system. This paper explains the methods of detecting the region of interest and the direction vector and proposes a method of interpolating and applying the dynamic weight in order to stabilize control. Lastly, qualitative and quantitative analyses are performed on the proposed 3D pointing method to verify its ability to deliver stable control.
Keywords
Depth image; Hand Mouse; Dynamic Weight; 3D Pointing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 F. Kirac, Y. E. Kara and L. Akarun, "Hierarchically constrained 3D hand pose estimation using regression forests from single frame depth data", International Journal on Pattern Recognition Letters- Special Issue on Depth Image Analysis, September 2013.
2 S. P. Priyal and P. K. Bora, "A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments", International Journal on Pattern Recognition, Vol. 46, No. 8, pp. 2202-2219, August 2013.   DOI   ScienceOn
3 Z. Song, H. Yang, Y. Zhao and F. Zheng, "Hand Detection and Gesture Recognition Exploit Motion Times Image in Complicate Scenarios", In proceedings of Advances in Visual Computing - 6th International Symposium, ISVC 2010, Vol. 6454, pp. 628-636, November 2010.
4 S. Malassiotis and M. G. Strintzis, "Real-time hand posture recognition using range data", International Journal on Image and Vision Computing, Vol. 26, No. 7, pp. 1027-1037, July 2008.   DOI   ScienceOn
5 P. Suryanarayan, A. Subramanian and D. Mandalapu, "Dynamic Hand Pose Recognition using Depth Data", In proceedings of the 2010 International Conference on Pattern Recognition, pp. 3105-3108, August 2010.
6 F. Dominio, M. Donadeo and P. Zanuttigh, "Combining multiple depth-based descriptors for hand gesture recognition", International Journal on Pattern Recognition Letters, October 2013.
7 N. D. Binh, E. Shuichi and T. Ejima, "Real-time hand tracking and gesture recognition system", In proceedings of International Conference on Graphics, Vision and Image Processing, pp. 362-368, December 2005.
8 A. Kurakin, Z. Zhang and Z. Liu, "A Real Time System for Dynamic Hand Gesture Recognition with a Depth Sensor", In proceedings of the 20th European Signal Processing Conference, pp. 1975-1979, August 2012.
9 M. Elmezain, A. Al-Hamadi and B. Michaelis, "Hand trajectory-based gesture spotting and recognition using HMM", In proceedings of the International Conference on Image Processing, pp. 3577-3580, November 2009.
10 X. Liu and K. Fujimura, "Hand Gesture Recognition using Depth Data", In proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 529-534, May 2004.
11 H. J. Park, "A Method for Controlling Mouse Movement using a Real-Time Camera", Brown University, Providence, RI, USA, Department of Computer Science, 2010.
12 D. D. Luong, S. Lee and T. S. Kim, "Human Computer Interface Using the Recognized Finger Parts of Hand Depth Silhouette via Random Forests", In proceedings of the 2013 13th International Conference on Control Automation and Systems, pp. 905-909, October 2013.
13 A. Aksac, O. Ozturk and T. Ozyer, "Real-time Multi-Objective Hand Posture/Gesture Recognition by Using Distance Classifiers and Finite State Machine for Virtual Mouse Operations", In proceedings of the 2011 7th International Conference on Electrical and Electronics Engineering, pp. II-457-II-461, December 2011.
14 S. I. Joo, "Dynamic Soft Cascade : Application to Gesture Recognition", Ph.D. Thesis, University of Soongsil, June 2014.
15 S. I. Joo, S. H. Weon and H. I. Choi, "Real-Time Depth-Based Hand Detection and Tracking", The Scientific World Journal, Vol. 2014, Article ID 284827, pp. 1-17, March 2014.
16 S. J. Miller, "The Method of Least Squares", Mathematics Department Brown University (2006): 1-7.
17 T. G. Zimmerman, J. Lanier, C. Blanchard, S. Bryson, and Y. Harvill, "A Hand Gesture Interface Device", CHI'87 In proceedings of the SIGCHI/GI Conference on Human Factors in Computing Systems and Graphics Interface, pp. 189-192, May 1987.
18 D. Eberly, "Least Squares Fitting of Data", http://www.geometrictools.com/, 1999.
19 http://www.cyberglovesystems.com/products/cyberglove-ii/overview
20 D. J. Sturman and D. Zeltzer, "A Survey of Glove-based Input", IEEE Computer Graphics and Applications, Vol. 14, No. 1, pp. 30-39, January 1994.   DOI   ScienceOn
21 http://www.dh.aist.go.jp/en/research/centered/dhand-link2/
22 R. Y. Wang and J. Popovic, "Real-Time Hand-Tracking with a Color Glove", ACM Transaction on Graphics(TOG), Vol. 28, No. 3, Article 63, August 2009.
23 D. Kim, O. Hilliges, S. Izadi, A. Butler, J. Chen, I. Oikonomidis and P. Olivier, "Digits : Freehand 3D Interactions Anywhere Using a Wrist-Worn Gloveless Sensor", In proceedings of the 25th annual ACM symposium on User Interface Software and Technology, pp. 167-176, October 2012.