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An Enhanced Computer Vision Based Hand Movement Capturing System with Stereo Vision
Manaram Gnanasekera, Nalan Karunanayake
Pages - 1 - 7     |    Revised - 29-02-2016     |    Published - 01-04-2016
Volume - 10   Issue - 1    |    Publication Date - April 2016  Table of Contents
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KEYWORDS
Computer Vision, Object tracking, Meanshift, Stereo Vision.
ABSTRACT
This framework is a hand movement capturing method which could be done in three different depth levels. The algorithm has the capability of capturing and identifying when the hand is moving up, down, right and left. From these captured movements four signals could be generated. Moreover, when these hand movements are done, 15cm-75cm, 75cm-100cm, 100cm- 200cm from the camera (3 depth levels), twelve different signals could be generated. These generated signals could be used for applications such as game controlling (gaming).The existing method uses an object area based method for depth analysis. The results of the proposed work shows it has high accuracy compared to the existing method when tested for depth analysis.
CITED BY (3)  
1 Dhule, C., & Nagrare, T. (2014, April). Computer Vision Based Human-Computer Interaction Using Color Detection Techniques. In Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on (pp. 934-938). IEEE.
2 Kadu, A. A., & S Nagdive, A. (2014). “Real-Time 3D Game Using Sixth Sense and Haptic Technology”: A Review. IJRCCT, 3(1), 042-047.
3 Dhule, C. A., & Nagrare, T. H. (2013). Design Of Virtual Sense Technology For System Interface. IJRCCT, 2(12), 1454-1459.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
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1 M. Gnanasekera, "Computer Vision Based Hand Movement Capturing System," in The 8th International Conference on Computer Science & Education (ICCSE 2013), Colombo, 2013.
2 D. Comaniciu et al, "Real-Time Tracking of Non-Rigid Objects using Mean Shift," in Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head, 2000.
3 G. R. Bradski, "Computer Vision Face Tracking for Use in a Perceptual User Interface", Microcomputer Research Lab, Santa Clara, 2002.
4 Richard Szeliski. “Stereo Correspondence” in Computer Vision: Algorithms and Applications, 1st ed., vol. 1, Springer 2010, pp.537-571.
5 Myron Z. Brown, Darius Burschka, “Advances in Computational Stereo” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp.993-1008, August, 2003.
6 Samarath Brahmbhatt, “3D Geometry and Stereo Vision” in Practical OpenCV,1st ed., vol 1, Apress 2013, pp. 173-200.
7 U. R Dhond., J. K. Aggarwal, “Structure from Stereo.” IEEE Transaction on Systems, Man and Cybernatics, vol. 19, pp. 1489-1510, November 1989.
8 Vibin N. Valsan and C.Y Patil. “A System on Chip based Stereo Vision Approach for Disparity Measurement,” in Proc. 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, pp. 1284-1287.
9 Rong Xiang, Tao Hong, Ming Zhou. “Analysis of Depth Measurement Errors of Tomatoes Using Binocular Stereo Vision Based on Single Factor Experiments,” in Proc. 13th International Conference on Control, Automation, Robotics & Vision (ICARCV), December 2014, pp. 88-93.
10 G. Balakrishnan, G. Sainarayanan, R. Nagarajan “Stereo Image Sonification for Blind Navigation”, Tamkang Journal of Science and Engineering, vol. 10, pp. 67_76, 2007.
Dr. Manaram Gnanasekera
Department of Electrical and Computer Engineering Sri Lanka Institute of Information Technology Malabe, 10115 - Sri Lanka
manaramcv@gmail.com
Mr. Nalan Karunanayake
University of Sri Jayawardenapura - Sri Lanka