Hand Gesture Recognition for Real Time Human Machine Interaction System
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Poonam Sonwalkar, Tanuja Sakhare, Ashwini Patil, Sonal Kale
|DOI : 10.14445/22315381/IJETT-V19P245|
Poonam Sonwalkar, Tanuja Sakhare, Ashwini Patil, Sonal Kale "Hand Gesture Recognition for Real Time Human Machine Interaction System", International Journal of Engineering Trends and Technology (IJETT), V19(5), 262-264 Jan 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Real Time Human-machine Interaction system using hand gesture Recognition to handle the mouse event , media player , image viewer .Users have to repeat same mouse and keyboard actions, inducing waste of time. Gestures have long been considered as an interaction technique that can potentially deliver more natural. A fast gesture recognition scheme is proposed to be an interface for the human-machine interaction (HMI) of systems. The system presents some low-complexity algorithms and gestures to reduce the gesture recognition complexity and be more suitable for controlling real-time computer systems. In this paper we use the webcam for capturing the image. After capturing the image it converts into the binary image. A gesture is a specific combination of hand position.
 Y. Wu, T. Huang, Vision-based gesture recognition: a review, in gesture-based communications in HCI, Lecture Notes in Computer Science, Vol. 1739, Springer, Berlin, pp. 103–115, 1999.
 C. Pickering, K. Burnham, and M. Richardson, A research study of hand gesture recognition Technologies and Applications for Human Vehicle Interaction, in Proc. of the 3rd Institution of Engineering and Technology Conference on Automotive Electronics, pp. 1–15, 2007.
 W. Wierwille, Visual and manual demands of in car controls and displays. In Automotive Ergonomics, Ed. by Peacock, B.Karwowski, B., Taylor and Francis, pp. 299–313, 1993.
 C. Pickering, The search for a safer driver interface: a review of gesture recognition human machine interface, Computing & Control Engineering Journal, Vol. 16, pp. 34–40, 2005.
 L., Wang, W. Hu, and T. Tan, Recent developments in human motion analysis, Pattern Recognition, Vol. 36, pp. 585-601, 2003.
 K. Imagawa, S. Lu, and S. Igi, Color-based hands tracking system for sign language recognition, in Proc. of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 462–467, 1998.
 L. Bretzner, I. Laptev1, and T. Lindeberg, Hand gesture recognition using multi-scale colour features, Hierarchical Models and Particle Filtering, in Proc. of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 423, 2002.
 A. Yilmaz, and M. Shah, Actions as objects: a novel action representation, in Proc. of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
 A. El-Sawah, N. D. Georganas, and E. M. Petriu, A Prototype for 3D Hand Tracking and Posture Estimation, IEEE Transactions on Instrumentation and Measurement, Vol. 57, pp. 1627–1636, 2008.
 W. Lu, and J. Little, Tracking and recognizing actions at a distance, in Proc. of the ECCV Workshop on Computer Vision Based Analysis in Sport Environments, pp. 49–60, 2006.
 W. James and J. Little, Simultaneous tracking and action recognition using the PCA-HOG descriptor, in Proc. of the Third Canadian Conference on Computer and Robot Vision, pp. 6, 2006.
 L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, Actions as space-time shapes, IEEE Trans. Pattern Analysis and Machine Intelligence Vol. 29, No. 12, pp. 2247–2253, 2007.
 S. Calderara, R. Cucchiara, and A. Prati, Action signature: A novel holistic representation for action recognition, in Proc. of theInternational Conference on Advanced Video and Signal Based Surveillance, IEEE Computer Society Press, Washington, pp. 121128, 2008.
 C. Schuldt, I. Laptev, and B. Caputo, Recognizing human actions: a local svm approach, in Proc. of the International Conference on Pattern Recognition, Vol. 3, IEEE Computer Society Press, Cambridge, UK, pp. 32–36, 2004.
 P. Scovanner, S. Ali., and M. Shah, A 3-dimensional sift descriptorand its application to action recognition, in Proc. of the 15th international conference on Multimedia, ACM, pp. 357–360, 2007.
 J. Liu, and M. Shah, Learning human actions via information maximization, in Proc. of the International Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Anchorage, Alaska, pp. 1–8, 2008.
 Q. Luo, X. Kong, G. Zeng, and J. Fan, Human action detection via boosted local motion histograms, Machine Vision and Applications, Vol. 21, pp. 377–389, 2010.
 N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Sys., Man., Cyber. Vol. 9, pp. 62–66, 1979.
 M. B. Dillencourt and H. Samet and M. Tamminen, A general approach to connected-component labeling for arbitrary image representations, Journal of the ACM, Vol. 39, pp. 253–280, 1992.
Gesture Recognition, Human Machine Interaction System, Webcam.